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Author : Jack Sklansky
Publisher :
ISBN 13 :
Total Pages : 452 pages
Book Rating : 4.22/5 ( download)
Download or read book Pattern Recognition: Introduction and Foundations written by Jack Sklansky and published by . This book was released on 1973 with total page 452 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Syed Thouheed Ahmed
Publisher : MileStone Research Publications
ISBN 13 : 9354931375
Total Pages : 156 pages
Book Rating : 4.76/5 ( download)
Download or read book PATTERN RECOGNITION written by Syed Thouheed Ahmed and published by MileStone Research Publications. This book was released on 2021-08-01 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the primary and supportive topics on pattern recognition with respect to beginners understand-ability. The aspects of pattern recognition is value added with an introductory of machine learning terminologies. This book covers the aspects of pattern validation, recognition, computation and processing. The initial aspects such as data representation and feature extraction is reported with supportive topics such as computational algorithms and decision trees. This text book covers the aspects as reported. Par t - I In this part, the initial foundation aspects of pattern recognition is discussed with reference to probabilities role in influencing a pattern occurrence, pattern extraction and properties. Introduction: Definition of Pattern Recognition, Applications, Datasets for Pattern Recognition, Different paradigms for Pattern Recognition, Introduction to probability, events, random variables, Joint distributions and densities, moments. Estimation minimum risk estimators, problems. Representation: Data structures for Pattern Recognition, Representation of clusters, proximity measures, size of patterns, Abstraction of Data set, Feature extraction, Feature selection, Evaluation. Par t - II In Part - II of the text, the mathematical representation and computation algorithms for extracting and evaluating patterns are discussed. The basic algorithms of machine learning classifiers with Nearest neighbor and Naive Bayes is reported with value added validation process using decision trees. Computational Algorithms: Nearest neighbor algorithm, variants of NN algorithms, use of NN for transaction databases, efficient algorithms, Data reduction, prototype selection, Bayes theorem, minimum error rate classifier, estimation of probabilities, estimation of probabilities, comparison with NNC, Naive Bayesclassifier, Bayesian belief network. Decision Trees: Introduction, Decision Tree for Pattern Recognition, Construction of Decision Tree, Splittingat the nodes, Over-fitting& Pruning, Examples.
Author : Christopher M. Bishop
Publisher : Springer
ISBN 13 : 9781493938438
Total Pages : 0 pages
Book Rating : 4.36/5 ( download)
Download or read book Pattern Recognition and Machine Learning written by Christopher M. Bishop and published by Springer. This book was released on 2016-08-23 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
Author : Jürgen Beyerer
Publisher : Walter de Gruyter GmbH & Co KG
ISBN 13 : 3110537966
Total Pages : 311 pages
Book Rating : 4.63/5 ( download)
Download or read book Pattern Recognition written by Jürgen Beyerer and published by Walter de Gruyter GmbH & Co KG. This book was released on 2017-12-04 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book offers a thorough introduction to Pattern Recognition aimed at master and advanced bachelor students of engineering and the natural sciences. Besides classification - the heart of Pattern Recognition - special emphasis is put on features, their typology, their properties and their systematic construction. Additionally, general principles that govern Pattern Recognition are illustrated and explained in a comprehensible way. Rather than presenting a complete overview over the rapidly evolving field, the book is to clarifies the concepts so that the reader can easily understand the underlying ideas and the rationale behind the methods. For this purpose, the mathematical treatment of Pattern Recognition is pushed so far that the mechanisms of action become clear and visible, but not farther. Therefore, not all derivations are driven into the last mathematical detail, as a mathematician would expect it. Ideas of proofs are presented instead of complete proofs. From the authors’ point of view, this concept allows to teach the essential ideas of Pattern Recognition with sufficient depth within a relatively lean book. Mathematical methods explained thoroughly Extremely practical approach with many examples Based on over ten years lecture at Karlsruhe Institute of Technology For students but also for practitioners
Author : Michal P. Chytil
Publisher : Springer Science & Business Media
ISBN 13 : 9783540501107
Total Pages : 582 pages
Book Rating : 4.0X/5 ( download)
Download or read book Mathematical Foundations of Computer Science 1988 written by Michal P. Chytil and published by Springer Science & Business Media. This book was released on 1988-08-10 with total page 582 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains 11 invited lectures and 42 communications presented at the 13th Conference on Mathematical Foundations of Computer Science, MFCS '88, held at Carlsbad, Czechoslovakia, August 29 - September 2, 1988. Most of the papers present material from the following four fields: - complexity theory, in particular structural complexity, - concurrency and parellelism, - formal language theory, - semantics. Other areas treated in the proceedings include functional programming, inductive syntactical synthesis, unification algorithms, relational databases and incremental attribute evaluation.
Author : Menahem Friedman
Publisher : World Scientific
ISBN 13 : 9789810233129
Total Pages : 350 pages
Book Rating : 4.24/5 ( download)
Download or read book Introduction to Pattern Recognition written by Menahem Friedman and published by World Scientific. This book was released on 1999 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an introduction to pattern recognition, meant for undergraduate and graduate students in computer science and related fields in science and technology. Most of the topics are accompanied by detailed algorithms and real world applications. In addition to statistical and structural approaches, novel topics such as fuzzy pattern recognition and pattern recognition via neural networks are also reviewed. Each topic is followed by several examples solved in detail. The only prerequisites for using this book are a one-semester course in discrete mathematics and a knowledge of the basic preliminaries of calculus, linear algebra and probability theory.
Author : Ulisses Braga-Neto
Publisher : Springer Nature
ISBN 13 : 3030276562
Total Pages : 357 pages
Book Rating : 4.60/5 ( download)
Download or read book Fundamentals of Pattern Recognition and Machine Learning written by Ulisses Braga-Neto and published by Springer Nature. This book was released on 2020-09-10 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fundamentals of Pattern Recognition and Machine Learning is designed for a one or two-semester introductory course in Pattern Recognition or Machine Learning at the graduate or advanced undergraduate level. The book combines theory and practice and is suitable to the classroom and self-study. It has grown out of lecture notes and assignments that the author has developed while teaching classes on this topic for the past 13 years at Texas A&M University. The book is intended to be concise but thorough. It does not attempt an encyclopedic approach, but covers in significant detail the tools commonly used in pattern recognition and machine learning, including classification, dimensionality reduction, regression, and clustering, as well as recent popular topics such as Gaussian process regression and convolutional neural networks. In addition, the selection of topics has a few features that are unique among comparable texts: it contains an extensive chapter on classifier error estimation, as well as sections on Bayesian classification, Bayesian error estimation, separate sampling, and rank-based classification. The book is mathematically rigorous and covers the classical theorems in the area. Nevertheless, an effort is made in the book to strike a balance between theory and practice. In particular, examples with datasets from applications in bioinformatics and materials informatics are used throughout to illustrate the theory. These datasets are available from the book website to be used in end-of-chapter coding assignments based on python and scikit-learn. All plots in the text were generated using python scripts, which are also available on the book website.
Author :
Publisher :
ISBN 13 : 9814479144
Total Pages : pages
Book Rating : 4.41/5 ( download)
Download or read book The Dissimilarity Representation for Pattern Recognition written by and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Menahem Friedman
Publisher : World Scientific Publishing Company
ISBN 13 : 9813105186
Total Pages : 344 pages
Book Rating : 4.88/5 ( download)
Download or read book Introduction to Pattern Recognition written by Menahem Friedman and published by World Scientific Publishing Company. This book was released on 1999-03-01 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an introduction to pattern recognition, meant for undergraduate and graduate students in computer science and related fields in science and technology. Most of the topics are accompanied by detailed algorithms and real world applications. In addition to statistical and structural approaches, novel topics such as fuzzy pattern recognition and pattern recognition via neural networks are also reviewed. Each topic is followed by several examples solved in detail. The only prerequisites for using this book are a one-semester course in discrete mathematics and a knowledge of the basic preliminaries of calculus, linear algebra and probability theory.
Author : Christopher M. Bishop
Publisher : Oxford University Press
ISBN 13 : 0198538642
Total Pages : 501 pages
Book Rating : 4.46/5 ( download)
Download or read book Neural Networks for Pattern Recognition written by Christopher M. Bishop and published by Oxford University Press. This book was released on 1995-11-23 with total page 501 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical pattern recognition; Probability density estimation; Single-layer networks; The multi-layer perceptron; Radial basis functions; Error functions; Parameter optimization algorithms; Pre-processing and feature extraction; Learning and generalization; Bayesian techniques; Appendix; References; Index.
