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BT 34.016 353.402 Td /F1 19.5 Tf [(Pattern Classification Duda Second Edition)] TJ ET
BT 34.016 316.301 Td /F1 9.8 Tf [(Yeah, reviewing a book )] TJ ET
BT 138.068 316.301 Td /F1 9.8 Tf [(Pattern Classification Duda Second Edition)] TJ ET
BT 323.942 316.301 Td /F1 9.8 Tf [( could accumulate your near friends listings. This is )] TJ ET
BT 34.016 304.396 Td /F1 9.8 Tf [(just one of the solutions for you to be successful. As understood, triumph does not recommend that you have )] TJ ET
BT 34.016 292.491 Td /F1 9.8 Tf [(extraordinary points. )] TJ ET
BT 34.016 268.887 Td /F1 9.8 Tf [(Comprehending as without difficulty as settlement even more than new will allow each success. adjacent to, the )] TJ ET
BT 517.391 268.887 Td /F1 9.8 Tf [(message )] TJ ET
BT 34.016 256.982 Td /F1 9.8 Tf [(as competently as sharpness of this Pattern Classification Duda Second Edition can be taken as skillfully as picked to )] TJ ET
BT 34.016 245.077 Td /F1 9.8 Tf [(act.)] TJ ET
BT 34.016 201.972 Td /F1 9.8 Tf [(Introduction to Pattern Recognition and Machine Learning)] TJ ET
BT 283.313 201.972 Td /F1 9.8 Tf [( M Narasimha Murty 2015-04-22 This book adopts a detailed )] TJ ET
BT 34.016 190.068 Td /F1 9.8 Tf [(and methodological algorithmic approach to explain the concepts of pattern recognition. While the text provides a )] TJ ET
BT 34.016 178.163 Td /F1 9.8 Tf [(systematic account of its major topics such as pattern representation and nearest neighbour based classifiers, current )] TJ ET
BT 34.016 166.258 Td /F1 9.8 Tf [(topics — neural networks, support vector machines and decision trees — attributed to the recent vast progress in this )] TJ ET
BT 34.016 154.353 Td /F1 9.8 Tf [(field are also dealt with. Introduction to Pattern Recognition and Machine Learning will equip readers, especially senior )] TJ ET
BT 34.016 142.449 Td /F1 9.8 Tf [(computer science undergraduates, with a deeper understanding of the subject matter. Contents:IntroductionTypes of )] TJ ET
BT 34.016 130.544 Td /F1 9.8 Tf [(DataFeature Extraction and Feature SelectionBayesian LearningClassificationClassification Using Soft Computing )] TJ ET
BT 34.016 118.639 Td /F1 9.8 Tf [(TechniquesData ClusteringSoft ClusteringApplication — Social and Information Networks Readership: Academics and )] TJ ET
BT 34.016 106.734 Td /F1 9.8 Tf [(working professionals in computer science. Key Features:The algorithmic approach taken and the practical issues dealt )] TJ ET
BT 34.016 94.830 Td /F1 9.8 Tf [(with will aid the reader in writing programs and implementing methodsCovers recent and advanced topics by providing )] TJ ET
BT 34.016 82.925 Td /F1 9.8 Tf [(working exercises, examples and illustrations in each chapterProvides the reader with a deeper understanding of the )] TJ ET
BT 34.016 71.020 Td /F1 9.8 Tf [(subject matterKeywords:Clustering;Classification;Supervised Learning;Soft Computing)] TJ ET
BT 34.016 59.115 Td /F1 9.8 Tf [(Fuzzy Models and Algorithms for Pattern Recognition and Image Processing)] TJ ET
BT 364.024 59.115 Td /F1 9.8 Tf [( James C. Bezdek 2006-09-28 Fuzzy )] TJ ET
BT 34.016 47.211 Td /F1 9.8 Tf [(Models and Algorithms for Pattern Recognition and Image Processing presents a comprehensive introduction of the use )] TJ ET
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BT 34.016 375.990 Td /F1 9.8 Tf [(of fuzzy models in pattern recognition and selected topics in image processing and computer vision. Unique to this )] TJ ET
BT 34.016 364.086 Td /F1 9.8 Tf [(volume in the Kluwer Handbooks of Fuzzy Sets Series is the fact that this book was written in its entirety by its four )] TJ ET
BT 34.016 352.181 Td /F1 9.8 Tf [(authors. A single notation, presentation style, and purpose are used throughout. The result is an extensive unified )] TJ ET
BT 34.016 340.276 Td /F1 9.8 Tf [(treatment of many fuzzy models for pattern recognition. The main topics are clustering and classifier design, with )] TJ ET
BT 34.016 328.371 Td /F1 9.8 Tf [(extensive material on feature analysis relational clustering, image processing and computer vision. Also included are )] TJ ET
BT 34.016 316.467 Td /F1 9.8 Tf [(numerous figures, images and numerical examples that illustrate the use of various models involving applications in )] TJ ET
BT 34.016 304.562 Td /F1 9.8 Tf [(medicine, character and word recognition, remote sensing, military image analysis, and industrial engineering.)] TJ ET
BT 34.016 292.657 Td /F1 9.8 Tf [(Pattern Classification)] TJ ET
BT 125.588 292.657 Td /F1 9.8 Tf [( Richard O. Duda 2012-11-09 The first edition, published in 1973, has become a classicreference in )] TJ ET
BT 34.016 280.752 Td /F1 9.8 Tf [(the field. Now with the second edition, readers willfind information on key new topics such as neural networks )] TJ ET
BT 34.016 268.848 Td /F1 9.8 Tf [(andstatistical pattern recognition, the theory of machine learning,and the theory of invariances. Also included are worked )] TJ ET
BT 34.016 256.943 Td /F1 9.8 Tf [(examples,comparisons between different methods, extensive graphics, expandedexercises and computer project topics. )] TJ ET
BT 34.016 245.038 Td /F1 9.8 Tf [(An Instructor's Manual presenting detailed solutions to all theproblems in the book is available from the Wiley )] TJ ET
BT 34.016 233.133 Td /F1 9.8 Tf [(editorialdepartment.)] TJ ET
BT 34.016 221.229 Td /F1 9.8 Tf [(Pattern Recognition)] TJ ET
BT 119.640 221.229 Td /F1 9.8 Tf [( Karina Mariela Figueroa Mora 2020-06-17 This book constitutes the proceedings of the 12th Mexican )] TJ ET
BT 34.016 209.324 Td /F1 9.8 Tf [(Conference on Pattern Recognition, MCPR 2020, which was due to be held in Morelia, Mexico, in June 2020. The )] TJ ET
BT 34.016 197.419 Td /F1 9.8 Tf [(conference was held virtually due to the COVID-19 pandemic. The 31 papers presented in this volume were carefully )] TJ ET
BT 34.016 185.514 Td /F1 9.8 Tf [(reviewed and selected from 67 submissions. They were organized in the following topical sections: pattern recognition )] TJ ET
BT 34.016 173.610 Td /F1 9.8 Tf [(techniques; image processing and analysis; computer vision; industrial and medical applications of pattern recognition; )] TJ ET
BT 34.016 161.705 Td /F1 9.8 Tf [(natural language processing and recognition; artificial intelligence techniques and recognition.)] TJ ET
BT 34.016 149.800 Td /F1 9.8 Tf [(Understanding Machine Learning)] TJ ET
BT 177.623 149.800 Td /F1 9.8 Tf [( Shai Shalev-Shwartz 2014-05-19 Introduces machine learning and its algorithmic )] TJ ET
BT 34.016 137.895 Td /F1 9.8 Tf [(paradigms, explaining the principles behind automated learning approaches and the considerations underlying their )] TJ ET
BT 34.016 125.991 Td /F1 9.8 Tf [(usage.)] TJ ET
BT 34.016 114.086 Td /F1 9.8 Tf [(Pattern Classification 2nd Edition with Computer Manual 2nd Edition Set)] TJ ET
BT 346.152 114.086 Td /F1 9.8 Tf [( Richard O. Duda 2004-06-04 The first edition, )] TJ ET
BT 34.016 102.181 Td /F1 9.8 Tf [(published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information )] TJ ET
BT 34.016 90.276 Td /F1 9.8 Tf [(on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the )] TJ ET
BT 34.016 78.372 Td /F1 9.8 Tf [(theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, )] TJ ET
BT 34.016 66.467 Td /F1 9.8 Tf [(expanded exercises and computer project topics. An Instructor's Manual presenting detailed solutions to all the problems )] TJ ET
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BT 34.016 375.990 Td /F1 9.8 Tf [(in the book is available from the Wiley editorial department.)] TJ ET
BT 34.016 364.086 Td /F1 9.8 Tf [(Advanced Lectures on Machine Learning)] TJ ET
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BT 211.232 364.086 Td /F1 9.8 Tf [( Olivier Bousquet 2011-03-22 Machine Learning has become a key enabling )] TJ ET
BT 34.016 352.181 Td /F1 9.8 Tf [(technology for many engineering applications, investigating scientific questions and theoretical problems alike. To )] TJ ET
BT 34.016 340.276 Td /F1 9.8 Tf [(stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the )] TJ ET
BT 34.016 328.371 Td /F1 9.8 Tf [(documentation of which is published as LNAI 2600. This book presents revised lectures of two subsequent summer )] TJ ET
BT 34.016 316.467 Td /F1 9.8 Tf [(schools held in 2003 in Canberra, Australia, and in Tübingen, Germany. The tutorial lectures included are devoted to )] TJ ET
BT 34.016 304.562 Td /F1 9.8 Tf [(statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they )] TJ ET
BT 34.016 292.657 Td /F1 9.8 Tf [(provide in-depth overviews of exciting new developments and contain a large number of references. Graduate students, )] TJ ET
BT 34.016 280.752 Td /F1 9.8 Tf [(lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine )] TJ ET
BT 34.016 268.848 Td /F1 9.8 Tf [(learning.)] TJ ET
BT 34.016 256.943 Td /F1 9.8 Tf [(Machine Learning)] TJ ET
BT 111.509 256.943 Td /F1 9.8 Tf [( Peter Flach 2012-09-20 Covering all the main approaches in state-of-the-art machine learning )] TJ ET
BT 34.016 245.038 Td /F1 9.8 Tf [(research, this will set a new standard as an introductory textbook.)] TJ ET
BT 34.016 233.133 Td /F1 9.8 Tf [(Ten Lectures on Statistical and Structural Pattern Recognition)] TJ ET
BT 300.639 233.133 Td /F1 9.8 Tf [( M.I. Schlesinger 2013-03-09 Preface to the English edition )] TJ ET
BT 34.016 221.229 Td /F1 9.8 Tf [(This monograph Ten Lectur,es on Statistical and Structural Pattern Recognition uncovers the close relationship between )] TJ ET
BT 34.016 209.324 Td /F1 9.8 Tf [(various well known pattern recognition problems that have so far been considered independent. These relationships )] TJ ET
BT 34.016 197.419 Td /F1 9.8 Tf [(became apparent when formal procedures addressing not only known prob lems but also their generalisations were )] TJ ET
BT 34.016 185.514 Td /F1 9.8 Tf [(discovered. The generalised problem formulations were analysed mathematically and unified algorithms were found. The )] TJ ET
BT 34.016 173.610 Td /F1 9.8 Tf [(book unifies of two main streams ill pattern recognition-the statisti cal a11d structural ones. In addition to this bridging on )] TJ ET
BT 34.016 161.705 Td /F1 9.8 Tf [(the uppermost level, the book mentions several other unexpected relations within statistical and structural methods. The )] TJ ET
BT 34.016 149.800 Td /F1 9.8 Tf [(monograph is intended for experts, for students, as well as for those who want to enter the field of pattern recognition. )] TJ ET
BT 34.016 137.895 Td /F1 9.8 Tf [(The theory is built up from scratch with almost no assumptions about any prior knowledge of the reader. Even when )] TJ ET
BT 34.016 125.991 Td /F1 9.8 Tf [(rigorous mathematical language is used we make an effort to keep the text easy to comprehend. This approach makes )] TJ ET
BT 34.016 114.086 Td /F1 9.8 Tf [(the book suitable for students at the beginning of their scientific career. Basic building blocks are explained in a style of )] TJ ET
BT 34.016 102.181 Td /F1 9.8 Tf [(an accessible intellectual exercise, thus promoting good practice in reading mathematical text. The paradoxes, beauty, )] TJ ET
BT 34.016 90.276 Td /F1 9.8 Tf [(and pitfalls of scientific research are shown on examples from pattern recognition. Each lecture is amended by a )] TJ ET
BT 34.016 78.372 Td /F1 9.8 Tf [(discussion with an inquisitive student that elucidates and deepens the explanation, providing additional pointers to )] TJ ET
BT 34.016 66.467 Td /F1 9.8 Tf [(computational procedures and deep rooted errors.)] TJ ET
BT 34.016 54.562 Td /F1 9.8 Tf [(Combining Pattern Classifiers)] TJ ET
BT 162.423 54.562 Td /F1 9.8 Tf [( Ludmila I. Kuncheva 2004-08-20 Covering pattern classification methods, Combining )] TJ ET
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BT 34.016 375.990 Td /F1 9.8 Tf [(Classifiers: Ideas and Methods focuses on the important and widely studied issue of how to combine several classifiers )] TJ ET
BT 34.016 364.086 Td /F1 9.8 Tf [(together in order to achieve improved recognition performance. It is one of the first books to provide unified, coherent, )] TJ ET
BT 34.016 352.181 Td /F1 9.8 Tf [(and expansive coverage of the topic and as such will be welcomed by those involved in the area. With case studies that )] TJ ET
BT 34.016 340.276 Td /F1 9.8 Tf [(bring the text alive and demonstrate 'real-world' applications it is destined to become essential reading.)] TJ ET
BT 34.016 328.371 Td /F1 9.8 Tf [(Image Analysis, Classification and Change Detection in Remote Sensing)] TJ ET
0.195 w 0 J [ ] 0 d
34.016 326.763 m 347.771 326.763 l S
BT 347.771 328.371 Td /F1 9.8 Tf [( Morton John Canty 2019-03-11 Image Analysis, )] TJ ET
BT 34.016 316.467 Td /F1 9.8 Tf [(Classification and Change Detection in Remote Sensing: With Algorithms for Python, Fourth Edition, is focused on the )] TJ ET
BT 34.016 304.562 Td /F1 9.8 Tf [(development and implementation of statistically motivated, data-driven techniques for digital image analysis of remotely )] TJ ET
BT 34.016 292.657 Td /F1 9.8 Tf [(sensed imagery and it features a tight interweaving of statistical and machine learning theory of algorithms with computer )] TJ ET
BT 34.016 280.752 Td /F1 9.8 Tf [(codes. It develops statistical methods for the analysis of optical/infrared and synthetic aperture radar \(SAR\) imagery, )] TJ ET
BT 34.016 268.848 Td /F1 9.8 Tf [(including wavelet transformations, kernel methods for nonlinear classification, as well as an introduction to deep learning )] TJ ET
BT 34.016 256.943 Td /F1 9.8 Tf [(in the context of feed forward neural networks. New in the Fourth Edition: An in-depth treatment of a recent sequential )] TJ ET
BT 34.016 245.038 Td /F1 9.8 Tf [(change detection algorithm for polarimetric SAR image time series. The accompanying software consists of Python \(open )] TJ ET
BT 34.016 233.133 Td /F1 9.8 Tf [(source\) versions of all of the main image analysis algorithms. Presents easy, platform-independent software installation )] TJ ET
BT 34.016 221.229 Td /F1 9.8 Tf [(methods \(Docker containerization\). Utilizes freely accessible imagery via the Google Earth Engine and provides many )] TJ ET
BT 34.016 209.324 Td /F1 9.8 Tf [(examples of cloud programming \(Google Earth Engine API\). Examines deep learning examples including TensorFlow )] TJ ET
BT 34.016 197.419 Td /F1 9.8 Tf [(and a sound introduction to neural networks, Based on the success and the reputation of the previous editions and )] TJ ET
BT 34.016 185.514 Td /F1 9.8 Tf [(compared to other textbooks in the market, Professor Canty’s fourth edition differs in the depth and sophistication of the )] TJ ET
BT 34.016 173.610 Td /F1 9.8 Tf [(material treated as well as in its consistent use of computer codes to illustrate the methods and algorithms discussed. It is )] TJ ET
BT 34.016 161.705 Td /F1 9.8 Tf [(self-contained and illustrated with many programming examples, all of which can be conveniently run in a web browser. )] TJ ET
BT 34.016 149.800 Td /F1 9.8 Tf [(Each chapter concludes with exercises complementing or extending the material in the text.)] TJ ET
BT 34.016 137.895 Td /F1 9.8 Tf [(Computer Vision)] TJ ET
BT 106.078 137.895 Td /F1 9.8 Tf [( Simon J. D. Prince 2012-06-18 A modern treatment focusing on learning and inference, with minimal )] TJ ET
BT 34.016 125.991 Td /F1 9.8 Tf [(prerequisites, real-world examples and implementable algorithms.)] TJ ET
BT 34.016 114.086 Td /F1 9.8 Tf [(Pattern Classification)] TJ ET
BT 125.588 114.086 Td /F1 9.8 Tf [( Shigeo Abe 2012-12-06 This book provides a unified approach for developing a fuzzy classifier and )] TJ ET
BT 34.016 102.181 Td /F1 9.8 Tf [(explains the advantages and disadvantages of different classifiers through extensive performance evaluation of real data )] TJ ET
BT 34.016 90.276 Td /F1 9.8 Tf [(sets. It thus offers new learning paradigms for analyzing neural networks and fuzzy systems, while training fuzzy )] TJ ET
BT 34.016 78.372 Td /F1 9.8 Tf [(classifiers. Function approximation is also treated and function approximators are compared.)] TJ ET
BT 34.016 66.467 Td /F1 9.8 Tf [(The Dissimilarity Representation for Pattern Recognition)] TJ ET
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BT 34.016 54.562 Td /F1 9.8 Tf [(Combining Artificial Neural Nets)] TJ ET
BT 171.627 54.562 Td /F1 9.8 Tf [( Amanda J.C. Sharkey 2012-12-06 This volume, written by leading researchers, presents )] TJ ET
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BT 34.016 375.990 Td /F1 9.8 Tf [(methods of combining neural nets to improve their performance. The techniques include ensemble-based approaches, )] TJ ET
BT 34.016 364.086 Td /F1 9.8 Tf [(where a variety of methods are used to create a set of different nets trained on the same task, and modular approaches, )] TJ ET
BT 34.016 352.181 Td /F1 9.8 Tf [(where a task is decomposed into simpler problems. The techniques are also accompanied by an evaluation of their )] TJ ET
BT 34.016 340.276 Td /F1 9.8 Tf [(relative effectiveness and their application to a variety of problems.)] TJ ET
BT 34.016 328.371 Td /F1 9.8 Tf [(A Probabilistic Theory of Pattern Recognition)] TJ ET
0.195 w 0 J [ ] 0 d
34.016 326.763 m 228.011 326.763 l S
BT 228.011 328.371 Td /F1 9.8 Tf [( Luc Devroye 2013-11-27 A self-contained and coherent account of )] TJ ET
BT 34.016 316.467 Td /F1 9.8 Tf [(probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis )] TJ ET
BT 34.016 304.562 Td /F1 9.8 Tf [(theory, parametric classification, and feature extraction. Each chapter concludes with problems and exercises to further )] TJ ET
BT 34.016 292.657 Td /F1 9.8 Tf [(the readers understanding. Both research workers and graduate students will benefit from this wide-ranging and up-to-)] TJ ET
BT 34.016 280.752 Td /F1 9.8 Tf [(date account of a fast- moving field.)] TJ ET
BT 34.016 268.848 Td /F1 9.8 Tf [(Handbook Of Pattern Recognition And Computer Vision \(2nd Edition\))] TJ ET
0.195 w 0 J [ ] 0 d
34.016 267.239 m 332.609 267.239 l S
BT 332.609 268.848 Td /F1 9.8 Tf [( Chi Hau Chen 1999-03-12 The very significant )] TJ ET
BT 34.016 256.943 Td /F1 9.8 Tf [(advances in computer vision and pattern recognition and their applications in the last few years reflect the strong and )] TJ ET
BT 34.016 245.038 Td /F1 9.8 Tf [(growing interest in the field as well as the many opportunities and challenges it offers. The second edition of this )] TJ ET
BT 34.016 233.133 Td /F1 9.8 Tf [(handbook represents both the latest progress and updated knowledge in this dynamic field. The applications and )] TJ ET
BT 34.016 221.229 Td /F1 9.8 Tf [(technological issues are particularly emphasized in this edition to reflect the wide applicability of the field in many )] TJ ET
BT 34.016 209.324 Td /F1 9.8 Tf [(practical problems. To keep the book in a single volume, it is not possible to retain all chapters of the first edition. )] TJ ET
BT 34.016 197.419 Td /F1 9.8 Tf [(However, the chapters of both editions are well written for permanent reference. This indispensable handbook will )] TJ ET
BT 34.016 185.514 Td /F1 9.8 Tf [(continue to serve as an authoritative and comprehensive guide in the field.)] TJ ET
BT 34.016 173.610 Td /F1 9.8 Tf [(Statistical Pattern Recognition)] TJ ET
0.195 w 0 J [ ] 0 d
34.016 172.001 m 164.071 172.001 l S
BT 164.071 173.610 Td /F1 9.8 Tf [( Andrew R. Webb 2003-07-25 Statistical pattern recognition is a very active area of study )] TJ ET
BT 34.016 161.705 Td /F1 9.8 Tf [(and research, which has seen many advances in recent years. New and emerging applications - such as data mining, )] TJ ET
BT 34.016 149.800 Td /F1 9.8 Tf [(web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition - require robust and )] TJ ET
BT 34.016 137.895 Td /F1 9.8 Tf [(efficient pattern recognition techniques. Statistical decision making and estimation are regarded as fundamental to the )] TJ ET
BT 34.016 125.991 Td /F1 9.8 Tf [(study of pattern recognition. Statistical Pattern Recognition, Second Edition has been fully updated with new methods, )] TJ ET
BT 34.016 114.086 Td /F1 9.8 Tf [(applications and references. It provides a comprehensive introduction to this vibrant area - with material drawn from )] TJ ET
BT 34.016 102.181 Td /F1 9.8 Tf [(engineering, statistics, computer science and the social sciences - and covers many application areas, such as database )] TJ ET
BT 34.016 90.276 Td /F1 9.8 Tf [(design, artificial neural networks, and decision support systems. * Provides a self-contained introduction to statistical )] TJ ET
BT 34.016 78.372 Td /F1 9.8 Tf [(pattern recognition. * Each technique described is illustrated by real examples. * Covers Bayesian methods, neural )] TJ ET
BT 34.016 66.467 Td /F1 9.8 Tf [(networks, support vector machines, and unsupervised classification. * Each section concludes with a description of the )] TJ ET
BT 34.016 54.562 Td /F1 9.8 Tf [(applications that have been addressed and with further developments of the theory. * Includes background material on )] TJ ET
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BT 34.016 375.990 Td /F1 9.8 Tf [(dissimilarity, parameter estimation, data, linear algebra and probability. * Features a variety of exercises, from 'open-book' )] TJ ET
BT 34.016 364.086 Td /F1 9.8 Tf [(questions to more lengthy projects. The book is aimed primarily at senior undergraduate and graduate students studying )] TJ ET
BT 34.016 352.181 Td /F1 9.8 Tf [(statistical pattern recognition, pattern processing, neural networks, and data mining, in both statistics and engineering )] TJ ET
BT 34.016 340.276 Td /F1 9.8 Tf [(departments. It is also an excellent source of reference for technical professionals working in advanced information )] TJ ET
BT 34.016 328.371 Td /F1 9.8 Tf [(development environments.)] TJ ET
BT 34.016 316.467 Td /F1 9.8 Tf [(Computer Methods in Image Analysis)] TJ ET
0.195 w 0 J [ ] 0 d
34.016 314.858 m 196.031 314.858 l S
BT 196.031 316.467 Td /F1 9.8 Tf [( J.K. Aggarwal 1977 )] TJ ET
BT 34.016 304.562 Td /F1 9.8 Tf [(Finite Element Method for Solids and Structures)] TJ ET
BT 241.018 304.562 Td /F1 9.8 Tf [( Sung W. Lee 2021-06-17 This innovative approach to teaching the finite )] TJ ET
BT 34.016 292.657 Td /F1 9.8 Tf [(element method blends theoretical, textbook-based learning with practical application using online and video resources. )] TJ ET
BT 34.016 280.752 Td /F1 9.8 Tf [(This hybrid teaching package features computational software such as MATLAB®, and tutorials presenting software )] TJ ET
BT 34.016 268.848 Td /F1 9.8 Tf [(applications such as PTC Creo Parametric, ANSYS APDL, ANSYS Workbench and SolidWorks, complete with detailed )] TJ ET
BT 34.016 256.943 Td /F1 9.8 Tf [(annotations and instructions so students can confidently develop hands-on experience. Suitable for senior undergraduate )] TJ ET
BT 34.016 245.038 Td /F1 9.8 Tf [(and graduate level classes, students will transition seamlessly between mathematical models and practical commercial )] TJ ET
BT 34.016 233.133 Td /F1 9.8 Tf [(software problems, empowering them to advance from basic differential equations to industry-standard modelling and )] TJ ET
BT 34.016 221.229 Td /F1 9.8 Tf [(analysis. Complete with over 120 end-of chapter problems and over 200 illustrations, this accessible reference will equip )] TJ ET
BT 34.016 209.324 Td /F1 9.8 Tf [(students with the tools they need to succeed in the workplace.)] TJ ET
BT 34.016 197.419 Td /F1 9.8 Tf [(Pattern Recognition and Neural Networks)] TJ ET
0.195 w 0 J [ ] 0 d
34.016 195.810 m 213.377 195.810 l S
BT 213.377 197.419 Td /F1 9.8 Tf [( Brian D. Ripley 2007 This 1996 book explains the statistical framework for )] TJ ET
BT 34.016 185.514 Td /F1 9.8 Tf [(pattern recognition and machine learning, now in paperback.)] TJ ET
BT 34.016 173.610 Td /F1 9.8 Tf [(Introduction to Pattern Recognition)] TJ ET
0.195 w 0 J [ ] 0 d
34.016 172.001 m 184.136 172.001 l S
BT 184.136 173.610 Td /F1 9.8 Tf [( Sergios Theodoridis 2010-03-03 Introduction to Pattern Recognition: A Matlab )] TJ ET
BT 34.016 161.705 Td /F1 9.8 Tf [(Approach is an accompanying manual to Theodoridis/Koutroumbas' Pattern Recognition. It includes Matlab code of the )] TJ ET
BT 34.016 149.800 Td /F1 9.8 Tf [(most common methods and algorithms in the book, together with a descriptive summary and solved examples, and )] TJ ET
BT 34.016 137.895 Td /F1 9.8 Tf [(including real-life data sets in imaging and audio recognition. This text is designed for electronic engineering, computer )] TJ ET
BT 34.016 125.991 Td /F1 9.8 Tf [(science, computer engineering, biomedical engineering and applied mathematics students taking graduate courses on )] TJ ET
BT 34.016 114.086 Td /F1 9.8 Tf [(pattern recognition and machine learning as well as R&D engineers and university researchers in image and signal )] TJ ET
BT 34.016 102.181 Td /F1 9.8 Tf [(processing/analyisis, and computer vision. Matlab code and descriptive summary of the most common methods and )] TJ ET
BT 34.016 90.276 Td /F1 9.8 Tf [(algorithms in Theodoridis/Koutroumbas, Pattern Recognition, Fourth Edition Solved examples in Matlab, including real-)] TJ ET
BT 34.016 78.372 Td /F1 9.8 Tf [(life data sets in imaging and audio recognition Available separately or at a special package price with the main text \(ISBN )] TJ ET
BT 34.016 66.467 Td /F1 9.8 Tf [(for package: 978-0-12-374491-3\))] TJ ET
BT 34.016 54.562 Td /F1 9.8 Tf [(Matrix Methods in Data Mining and Pattern Recognition, Second Edition)] TJ ET
BT 343.988 54.562 Td /F1 9.8 Tf [( Lars Elden 2019-08-30 This thoroughly revised )] TJ ET
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BT 34.016 375.990 Td /F1 9.8 Tf [(second edition provides an updated treatment of numerical linear algebra techniques for solving problems in data mining )] TJ ET
BT 34.016 364.086 Td /F1 9.8 Tf [(and pattern recognition. Adopting an application-oriented approach, the author introduces matrix theory and )] TJ ET
BT 34.016 352.181 Td /F1 9.8 Tf [(decompositions, describes how modern matrix methods can be applied in real life scenarios, and provides a set of tools )] TJ ET
BT 34.016 340.276 Td /F1 9.8 Tf [(that students can modify for a particular application. Building on material from the first edition, the author discusses basic )] TJ ET
BT 34.016 328.371 Td /F1 9.8 Tf [(graph concepts and their matrix counterparts. He introduces the graph Laplacian and properties of its eigenvectors )] TJ ET
BT 34.016 316.467 Td /F1 9.8 Tf [(needed in spectral partitioning and describes spectral graph partitioning applied to social networks and text classification. )] TJ ET
BT 34.016 304.562 Td /F1 9.8 Tf [(Examples are included to help readers visualize the results. This new edition also presents matrix-based methods that )] TJ ET
BT 34.016 292.657 Td /F1 9.8 Tf [(underlie many of the algorithms used for big data. The book provides a solid foundation to further explore related topics )] TJ ET
BT 34.016 280.752 Td /F1 9.8 Tf [(and presents applications such as classification of handwritten digits, text mining, text summarization, PageRank )] TJ ET
BT 34.016 268.848 Td /F1 9.8 Tf [(computations related to the Google search engine, and facial recognition. Exercises and computer assignments are )] TJ ET
BT 34.016 256.943 Td /F1 9.8 Tf [(available on a Web page that supplements the book. This book is primarily for undergraduate students who have )] TJ ET
BT 34.016 245.038 Td /F1 9.8 Tf [(previously taken an introductory scientific computing/numerical analysis course and graduate students in data mining and )] TJ ET
BT 34.016 233.133 Td /F1 9.8 Tf [(pattern recognition areas who need an introduction to linear algebra techniques.)] TJ ET
BT 34.016 221.229 Td /F1 9.8 Tf [(Pattern Recognition)] TJ ET
BT 119.640 221.229 Td /F1 9.8 Tf [( M. Narasimha Murty 2011-05-25 Observing the environment and recognising patterns for the )] TJ ET
BT 34.016 209.324 Td /F1 9.8 Tf [(purpose of decision making is fundamental to human nature. This book deals with the scientific discipline that enables )] TJ ET
BT 34.016 197.419 Td /F1 9.8 Tf [(similar perception in machines through pattern recognition \(PR\), which has application in diverse technology areas. This )] TJ ET
BT 34.016 185.514 Td /F1 9.8 Tf [(book is an exposition of principal topics in PR using an algorithmic approach. It provides a thorough introduction to the )] TJ ET
BT 34.016 173.610 Td /F1 9.8 Tf [(concepts of PR and a systematic account of the major topics in PR besides reviewing the vast progress made in the field )] TJ ET
BT 34.016 161.705 Td /F1 9.8 Tf [(in recent times. It includes basic techniques of PR, neural networks, support vector machines and decision trees. While )] TJ ET
BT 34.016 149.800 Td /F1 9.8 Tf [(theoretical aspects have been given due coverage, the emphasis is more on the practical. The book is replete with )] TJ ET
BT 34.016 137.895 Td /F1 9.8 Tf [(examples and illustrations and includes chapter-end exercises. It is designed to meet the needs of senior undergraduate )] TJ ET
BT 34.016 125.991 Td /F1 9.8 Tf [(and postgraduate students of computer science and allied disciplines.)] TJ ET
BT 34.016 114.086 Td /F1 9.8 Tf [(Introduction to Pattern Recognition)] TJ ET
0.195 w 0 J [ ] 0 d
34.016 112.477 m 184.136 112.477 l S
BT 184.136 114.086 Td /F1 9.8 Tf [( Menahem Friedman 1999 This book is an introduction to pattern recognition, meant )] TJ ET
BT 34.016 102.181 Td /F1 9.8 Tf [(for undergraduate and graduate students in computer science and related fields in science and technology. Most of the )] TJ ET
BT 34.016 90.276 Td /F1 9.8 Tf [(topics are accompanied by detailed algorithms and real world applications. In addition to statistical and structural )] TJ ET
BT 34.016 78.372 Td /F1 9.8 Tf [(approaches, novel topics such as fuzzy pattern recognition and pattern recognition via neural networks are also reviewed. )] TJ ET
BT 34.016 66.467 Td /F1 9.8 Tf [(Each topic is followed by several examples solved in detail. The only prerequisites for using this book are a one-semester )] TJ ET
BT 34.016 54.562 Td /F1 9.8 Tf [(course in discrete mathematics and a knowledge of the basic preliminaries of calculus, linear algebra and probability )] TJ ET
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BT 34.016 375.990 Td /F1 9.8 Tf [(theory.)] TJ ET
BT 34.016 364.086 Td /F1 9.8 Tf [(Structural, Syntactic, and Statistical Pattern Recognition)] TJ ET
0.195 w 0 J [ ] 0 d
34.016 362.477 m 275.162 362.477 l S
BT 275.162 364.086 Td /F1 9.8 Tf [( Dit-Yan Yeung 2006-08-03 This is the proceedings of the 11th )] TJ ET
BT 34.016 352.181 Td /F1 9.8 Tf [(International Workshop on Structural and Syntactic Pattern Recognition, SSPR 2006 and the 6th International Workshop )] TJ ET
BT 34.016 340.276 Td /F1 9.8 Tf [(on Statistical Techniques in Pattern Recognition, SPR 2006, held in Hong Kong, August 2006 alongside the Conference )] TJ ET
BT 34.016 328.371 Td /F1 9.8 Tf [(on Pattern Recognition, ICPR 2006. 38 revised full papers and 61 revised poster papers are included, together with 4 )] TJ ET
BT 34.016 316.467 Td /F1 9.8 Tf [(invited papers covering image analysis, character recognition, bayesian networks, graph-based methods and more.)] TJ ET
BT 34.016 304.562 Td /F1 9.8 Tf [(Principles of Distributed Database Systems)] TJ ET
BT 220.952 304.562 Td /F1 9.8 Tf [( M. Tamer Özsu 2011-02-24 This third edition of a classic textbook can be )] TJ ET
BT 34.016 292.657 Td /F1 9.8 Tf [(used to teach at the senior undergraduate and graduate levels. The material concentrates on fundamental theories as )] TJ ET
BT 34.016 280.752 Td /F1 9.8 Tf [(well as techniques and algorithms. The advent of the Internet and the World Wide Web, and, more recently, the )] TJ ET
BT 34.016 268.848 Td /F1 9.8 Tf [(emergence of cloud computing and streaming data applications, has forced a renewal of interest in distributed and )] TJ ET
BT 34.016 256.943 Td /F1 9.8 Tf [(parallel data management, while, at the same time, requiring a rethinking of some of the traditional techniques. This book )] TJ ET
BT 34.016 245.038 Td /F1 9.8 Tf [(covers the breadth and depth of this re-emerging field. The coverage consists of two parts. The first part discusses the )] TJ ET
BT 34.016 233.133 Td /F1 9.8 Tf [(fundamental principles of distributed data management and includes distribution design, data integration, distributed )] TJ ET
BT 34.016 221.229 Td /F1 9.8 Tf [(query processing and optimization, distributed transaction management, and replication. The second part focuses on )] TJ ET
BT 34.016 209.324 Td /F1 9.8 Tf [(more advanced topics and includes discussion of parallel database systems, distributed object management, peer-to-)] TJ ET
BT 34.016 197.419 Td /F1 9.8 Tf [(peer data management, web data management, data stream systems, and cloud computing. New in this Edition: • New )] TJ ET
BT 34.016 185.514 Td /F1 9.8 Tf [(chapters, covering database replication, database integration, multidatabase query processing, peer-to-peer data )] TJ ET
BT 34.016 173.610 Td /F1 9.8 Tf [(management, and web data management. • Coverage of emerging topics such as data streams and cloud computing • )] TJ ET
BT 34.016 161.705 Td /F1 9.8 Tf [(Extensive revisions and updates based on years of class testing and feedback Ancillary teaching materials are available.)] TJ ET
BT 34.016 149.800 Td /F1 9.8 Tf [(A Survey of Pattern Classification and Scene Analysis)] TJ ET
BT 267.031 149.800 Td /F1 9.8 Tf [( Richard O. Duda 1971 Pattern recognition is an essential part of )] TJ ET
BT 34.016 137.895 Td /F1 9.8 Tf [(artificial intelligence, and has been the subject of extensive research. The report gives a survey of the literature on pattern )] TJ ET
BT 34.016 125.991 Td /F1 9.8 Tf [(recognition. The survey is divided into two main parts, the first part devoted to statistical pattern recognition, and the )] TJ ET
BT 34.016 114.086 Td /F1 9.8 Tf [(second part devoted to pictorial pattern recognition. With the partial exception of waveform recognition, almost all of the )] TJ ET
BT 34.016 102.181 Td /F1 9.8 Tf [(work in pattern recognition falls into one or the other of these two categories. The bibliography includes more than 500 )] TJ ET
BT 34.016 90.276 Td /F1 9.8 Tf [(references. \(Author\).)] TJ ET
BT 34.016 78.372 Td /F1 9.8 Tf [(Hands-On Pattern Recognition)] TJ ET
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34.016 76.763 m 166.781 76.763 l S
BT 166.781 78.372 Td /F1 9.8 Tf [( Isabelle Guyon 2011-05-01 Recently organized competitions have been instrumental in )] TJ ET
BT 34.016 66.467 Td /F1 9.8 Tf [(pushing the state-of-the-art in machine learning, establishing benchmarks to fairly evaluate methods, and identifying )] TJ ET
BT 34.016 54.562 Td /F1 9.8 Tf [(techniques that really work. This volume in the Challenges in Machine Learning series harvests three years of effort of )] TJ ET
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BT 34.016 375.990 Td /F1 9.8 Tf [(hundreds of researchers who have participated in three competitions organized around five datasets from various )] TJ ET
BT 34.016 364.086 Td /F1 9.8 Tf [(application domains, designed to explore issues of data representation, model selection, and performance prediction.)] TJ ET
BT 34.016 352.181 Td /F1 9.8 Tf [(Neural Networks for Pattern Recognition)] TJ ET
BT 208.492 352.181 Td /F1 9.8 Tf [( Christopher M. Bishop 1995-11-23 `Readers will emerge with a rigorous )] TJ ET
BT 34.016 340.276 Td /F1 9.8 Tf [(statistical grounding in the theory of how to construct and train neural networks in pattern recognition' New Scientist)] TJ ET
BT 34.016 328.371 Td /F1 9.8 Tf [(Correlation Pattern Recognition)] TJ ET
BT 170.028 328.371 Td /F1 9.8 Tf [( B. V. K. Vijaya Kumar 2005-11-24 Correlation is a robust and general technique for )] TJ ET
BT 34.016 316.467 Td /F1 9.8 Tf [(pattern recognition and is used in many applications, such as automatic target recognition, biometric recognition and )] TJ ET
BT 34.016 304.562 Td /F1 9.8 Tf [(optical character recognition. The design, analysis and use of correlation pattern recognition algorithms requires )] TJ ET
BT 34.016 292.657 Td /F1 9.8 Tf [(background information, including linear systems theory, random variables and processes, matrix/vector methods, )] TJ ET
BT 34.016 280.752 Td /F1 9.8 Tf [(detection and estimation theory, digital signal processing and optical processing. This book provides a needed review of )] TJ ET
BT 34.016 268.848 Td /F1 9.8 Tf [(this diverse background material and develops the signal processing theory, the pattern recognition metrics, and the )] TJ ET
BT 34.016 256.943 Td /F1 9.8 Tf [(practical application know-how from basic premises. It shows both digital and optical implementations. It also contains )] TJ ET
BT 34.016 245.038 Td /F1 9.8 Tf [(technology presented by the team that developed it and includes case studies of significant interest, such as face and )] TJ ET
BT 34.016 233.133 Td /F1 9.8 Tf [(fingerprint recognition. Suitable for graduate students taking courses in pattern recognition theory, whilst reaching )] TJ ET
BT 34.016 221.229 Td /F1 9.8 Tf [(technical levels of interest to the professional practitioner.)] TJ ET
BT 34.016 209.324 Td /F1 9.8 Tf [(HAL's Legacy)] TJ ET
BT 93.861 209.324 Td /F1 9.8 Tf [( David G. Stork 1997 Collects essays concerning how close we are to building computers that are as )] TJ ET
BT 34.016 197.419 Td /F1 9.8 Tf [(intelligent, devious, and emotional as the computer in the classic film, 2001)] TJ ET
BT 34.016 185.514 Td /F1 9.8 Tf [(Pattern Recognition and Machine Learning)] TJ ET
BT 218.817 185.514 Td /F1 9.8 Tf [( Christopher M. Bishop 2016-08-23 This is the first textbook on pattern )] TJ ET
BT 34.016 173.610 Td /F1 9.8 Tf [(recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast )] TJ ET
BT 34.016 161.705 Td /F1 9.8 Tf [(approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability )] TJ ET
BT 34.016 149.800 Td /F1 9.8 Tf [(distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern )] TJ ET
BT 34.016 137.895 Td /F1 9.8 Tf [(recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is )] TJ ET
BT 34.016 125.991 Td /F1 9.8 Tf [(required, and some experience in the use of probabilities would be helpful though not essential as the book includes a )] TJ ET
BT 34.016 114.086 Td /F1 9.8 Tf [(self-contained introduction to basic probability theory.)] TJ ET
BT 34.016 102.181 Td /F1 9.8 Tf [(Neural Information Processing)] TJ ET
BT 165.143 102.181 Td /F1 9.8 Tf [( Chi-Sing Leung 2009-11-24 The two volumes LNCS 5863 and 5864 constitute the )] TJ ET
BT 34.016 90.276 Td /F1 9.8 Tf [(proceedings of the 16th International Conference on Neural Information Processing, ICONIP 2009, held in Bangkok, )] TJ ET
BT 34.016 78.372 Td /F1 9.8 Tf [(Thailand, in December 2009. The 145 regular session papers and 53 special session papers presented were carefully )] TJ ET
BT 34.016 66.467 Td /F1 9.8 Tf [(reviewed and selected from 466 submissions. The papers are structured in topical sections on cognitive science and )] TJ ET
BT 34.016 54.562 Td /F1 9.8 Tf [(computational neuroscience, neurodynamics, mathematical modeling and analysis, kernel and related methods, learning )] TJ ET
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BT 34.016 375.990 Td /F1 9.8 Tf [(algorithms, pattern analysis, face analysis and processing, image processing, financial applications, computer vision, )] TJ ET
BT 34.016 364.086 Td /F1 9.8 Tf [(control and robotics, evolutionary computation, other emerging computational methods, signal, data and text processing, )] TJ ET
BT 34.016 352.181 Td /F1 9.8 Tf [(artificial spiking neural systems: nonlinear dynamics and engineering applications, towards brain-inspired systems, )] TJ ET
BT 34.016 340.276 Td /F1 9.8 Tf [(computational advances in bioinformatics, data mining for cybersecurity, evolutionary neural networks: theory and )] TJ ET
BT 34.016 328.371 Td /F1 9.8 Tf [(practice, hybrid and adaptive systems for computer vision and robot control, intelligent data mining, neural networks for )] TJ ET
BT 34.016 316.467 Td /F1 9.8 Tf [(data mining, and SOM and related subjects and its applications.)] TJ ET
BT 34.016 304.562 Td /F1 9.8 Tf [(Materials Design Inspired by Nature)] TJ ET
BT 188.982 304.562 Td /F1 9.8 Tf [( Peter Fratzl 2015-11-09 The inner architecture of a material can have an astonishing )] TJ ET
BT 34.016 292.657 Td /F1 9.8 Tf [(effect on its overall properties and is vital to understand when designing new materials. Nature is a master at designing )] TJ ET
BT 34.016 280.752 Td /F1 9.8 Tf [(hierarchical structures and so researchers are looking at biological examples for inspiration, specifically to understand )] TJ ET
BT 34.016 268.848 Td /F1 9.8 Tf [(how nature arranges the inner architectures for a particular function in order to apply these design principles into man-)] TJ ET
BT 34.016 256.943 Td /F1 9.8 Tf [(made materials. Materials Design Inspired by Nature is the first book to address the relationship between the inner )] TJ ET
BT 34.016 245.038 Td /F1 9.8 Tf [(architecture of natural materials and their physical properties for materials design. The book explores examples from )] TJ ET
BT 34.016 233.133 Td /F1 9.8 Tf [(plants, the marine world, arthropods and bacteria, where the inner architecture is exploited to obtain specific mechanical, )] TJ ET
BT 34.016 221.229 Td /F1 9.8 Tf [(optical or magnetic properties along with how these design principles are used in man-made products. Details of the )] TJ ET
BT 34.016 209.324 Td /F1 9.8 Tf [(experimental methods used to investigate hierarchical structures are also given. Written by leading experts in bio-inspired )] TJ ET
BT 34.016 197.419 Td /F1 9.8 Tf [(materials research, this is essential reading for anyone developing new materials.)] TJ ET
BT 34.016 185.514 Td /F1 9.8 Tf [(Introduction to Machine Learning)] TJ ET
0.195 w 0 J [ ] 0 d
34.016 183.906 m 176.005 183.906 l S
BT 176.005 185.514 Td /F1 9.8 Tf [( Ethem Alpaydin 2014-08-29 The goal of machine learning is to program computers to )] TJ ET
BT 34.016 173.610 Td /F1 9.8 Tf [(use example data or past experience to solve a given problem. Many successful applications of machine learning exist )] TJ ET
BT 34.016 161.705 Td /F1 9.8 Tf [(already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a )] TJ ET
BT 34.016 149.800 Td /F1 9.8 Tf [(task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to )] TJ ET
BT 34.016 137.895 Td /F1 9.8 Tf [(Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in )] TJ ET
BT 34.016 125.991 Td /F1 9.8 Tf [(introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-)] TJ ET
BT 34.016 114.086 Td /F1 9.8 Tf [(parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel )] TJ ET
BT 34.016 102.181 Td /F1 9.8 Tf [(machines; graphical models; Bayesian estimation; and statistical testing.Machine learning is rapidly becoming a skill that )] TJ ET
BT 34.016 90.276 Td /F1 9.8 Tf [(computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects )] TJ ET
BT 34.016 78.372 Td /F1 9.8 Tf [(this shift, with added support for beginners, including selected solutions for exercises and additional example data sets )] TJ ET
BT 34.016 66.467 Td /F1 9.8 Tf [(\(with code available online\). Other substantial changes include discussions of outlier detection; ranking algorithms for )] TJ ET
BT 34.016 54.562 Td /F1 9.8 Tf [(perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel )] TJ ET
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BT 34.016 375.990 Td /F1 9.8 Tf [(algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning )] TJ ET
BT 34.016 364.086 Td /F1 9.8 Tf [(algorithms are explained so that students can easily move from the equations in the book to a computer program. The )] TJ ET
BT 34.016 352.181 Td /F1 9.8 Tf [(book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals )] TJ ET
BT 34.016 340.276 Td /F1 9.8 Tf [(who are concerned with the application of machine learning methods.)] TJ ET
BT 34.016 328.371 Td /F1 9.8 Tf [(Pattern Recognition)] TJ ET
BT 119.640 328.371 Td /F1 9.8 Tf [( Sergios Theodoridis 2003-05-15 Pattern recognition is a scientific discipline that is becoming )] TJ ET
BT 34.016 316.467 Td /F1 9.8 Tf [(increasingly important in the age of automation and information handling and retrieval. Patter Recognition, 2e covers the )] TJ ET
BT 34.016 304.562 Td /F1 9.8 Tf [(entire spectrum of pattern recognition applications, from image analysis to speech recognition and communications. This )] TJ ET
BT 34.016 292.657 Td /F1 9.8 Tf [(book presents cutting-edge material on neural networks, - a set of linked microprocessors that can form associations and )] TJ ET
BT 34.016 280.752 Td /F1 9.8 Tf [(uses pattern recognition to "learn" -and enhances student motivation by approaching pattern recognition from the )] TJ ET
BT 34.016 268.848 Td /F1 9.8 Tf [(designer's point of view. A direct result of more than 10 years of teaching experience, the text was developed by the )] TJ ET
BT 34.016 256.943 Td /F1 9.8 Tf [(authors through use in their own classrooms. *Approaches pattern recognition from the designer's point of view *New )] TJ ET
BT 34.016 245.038 Td /F1 9.8 Tf [(edition highlights latest developments in this growing field, including independent components and support vector )] TJ ET
BT 34.016 233.133 Td /F1 9.8 Tf [(machines, not available elsewhere *Supplemented by computer examples selected from applications of interest)] TJ ET
BT 34.016 221.229 Td /F1 9.8 Tf [(Introduction to Statistical Pattern Recognition)] TJ ET
BT 228.567 221.229 Td /F1 9.8 Tf [( Keinosuke Fukunaga 2013-10-22 This completely revised second edition )] TJ ET
BT 34.016 209.324 Td /F1 9.8 Tf [(presents an introduction to statistical pattern recognition. Pattern recognition in general covers a wide range of problems: )] TJ ET
BT 34.016 197.419 Td /F1 9.8 Tf [(it is applied to engineering problems, such as character readers and wave form analysis as well as to brain modeling in )] TJ ET
BT 34.016 185.514 Td /F1 9.8 Tf [(biology and psychology. Statistical decision and estimation, which are the main subjects of this book, are regarded as )] TJ ET
BT 34.016 173.610 Td /F1 9.8 Tf [(fundamental to the study of pattern recognition. This book is appropriate as a text for introductory courses in pattern )] TJ ET
BT 34.016 161.705 Td /F1 9.8 Tf [(recognition and as a reference book for workers in the field. Each chapter contains computer projects as well as )] TJ ET
BT 34.016 149.800 Td /F1 9.8 Tf [(exercises.)] TJ ET
BT 34.016 137.895 Td /F1 9.8 Tf [(Mathematical Methodologies in Pattern Recognition and Machine Learning)] TJ ET
0.195 w 0 J [ ] 0 d
34.016 136.287 m 355.912 136.287 l S
BT 355.912 137.895 Td /F1 9.8 Tf [( Pedro Latorre Carmona 2012-11-09 This )] TJ ET
BT 34.016 125.991 Td /F1 9.8 Tf [(volume features key contributions from the International Conference on Pattern Recognition Applications and Methods, )] TJ ET
BT 34.016 114.086 Td /F1 9.8 Tf [(\(ICPRAM 2012,\) held in Vilamoura, Algarve, Portugal from February 6th-8th, 2012. The conference provided a major )] TJ ET
BT 34.016 102.181 Td /F1 9.8 Tf [(point of collaboration between researchers, engineers and practitioners in the areas of Pattern Recognition, both from )] TJ ET
BT 34.016 90.276 Td /F1 9.8 Tf [(theoretical and applied perspectives, with a focus on mathematical methodologies. Contributions describe applications of )] TJ ET
BT 34.016 78.372 Td /F1 9.8 Tf [(pattern recognition techniques to real-world problems, interdisciplinary research, and experimental and theoretical studies )] TJ ET
BT 34.016 66.467 Td /F1 9.8 Tf [(which yield new insights that provide key advances in the field. This book will be suitable for scientists and researchers in )] TJ ET
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BT 34.016 375.990 Td /F1 9.8 Tf [(optimization, numerical methods, computer science, statistics and for differential geometers and mathematical physicists.)] TJ ET
BT 34.016 364.086 Td /F1 9.8 Tf [(Ten Lectures on Statistical and Structural Pattern Recognition)] TJ ET
BT 300.639 364.086 Td /F1 9.8 Tf [( M.I. Schlesinger 2002-05-31 This monograph explores the )] TJ ET
BT 34.016 352.181 Td /F1 9.8 Tf [(close relationship of various well-known pattern recognition problems that have so far been considered independent. )] TJ ET
BT 34.016 340.276 Td /F1 9.8 Tf [(These relationships became apparent with the discovery of formal procedures for addressing known problems and their )] TJ ET
BT 34.016 328.371 Td /F1 9.8 Tf [(generalisations. The generalised problem formulations were analysed mathematically and unified algorithms were found. )] TJ ET
BT 34.016 316.467 Td /F1 9.8 Tf [(The main scientific contribution of this book is the unification of two main streams in pattern recognition - the statistical )] TJ ET
BT 34.016 304.562 Td /F1 9.8 Tf [(one and the structural one. The material is presented in the form of ten lectures, each of which concludes with a )] TJ ET
BT 34.016 292.657 Td /F1 9.8 Tf [(discussion with a student. It provides new views and numerous original results in their field. Written in an easily )] TJ ET
BT 34.016 280.752 Td /F1 9.8 Tf [(accessible style, it introduces the basic building blocks of pattern recognition, demonstrates the beauty and the pitfalls of )] TJ ET
BT 34.016 268.848 Td /F1 9.8 Tf [(scientific research, and encourages good habits in reading mathematical text.)] TJ ET
BT 36.266 234.952 Td /F1 8.0 Tf [(pattern-classification-duda-second-edition)] TJ ET
BT 299.782 235.160 Td /F1 8.0 Tf [(Downloaded from )] TJ ET
BT 364.694 234.952 Td /F1 8.0 Tf [(photos.decemberists.com)] TJ ET
BT 455.838 235.160 Td /F1 8.0 Tf [( on October 6, 2022 by guest)] TJ ET
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