Pattern Recognition
Download Pattern Recognition PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Pattern Recognition book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Pattern Recognition
Author: J.P. Marques de Sá
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Pattern recognition currently comprises a vast body of methods supporting the development of numerous applications in many different areas of activity. The generally recognized relevance of pattern recognition methods and techniques lies, for the most part, in the general trend of "intelligent" task emulation, which has definitely pervaded our daily life. Robot assisted manufacture, medical diagnostic systems, forecast of economic variables, exploration of Earth's resources, and analysis of satellite data are just a few examples of activity fields where this trend applies. The pervasiveness of pattern recognition has boosted the number of task specific methodologies and enriched the number of links with other disciplines. As counterbalance to this dispersive tendency there have been, more recently, new theoretical developments that are bridging together many of the classical pattern recognition methods and presenting a new perspective of their links and inner workings. This book has its origin in an introductory course on pattern recognition taught at the Electrical and Computer Engineering Department, Oporto University. From the initial core of this course, the book grew with the intent of presenting a comprehensive and articulated view of pattern recognition methods combined with the intent of clarifying practical issues with the aid of examples and applications to real-life data. The book is primarily addressed to undergraduate and graduate students attending pattern recognition courses of engineering and computer science curricula.
PATTERN RECOGNITION
Author: Syed Thouheed Ahmed
language: en
Publisher: MileStone Research Publications
Release Date: 2021-08-01
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.
Pattern Recognition: From Classical To Modern Approaches
This volume, containing contributions by experts from all over the world, is a collection of 21 articles which present review and research material describing the evolution and recent developments of various pattern recognition methodologies, ranging from statistical, syntactic/linguistic, fuzzy-set-theoretic, neural, genetic-algorithmic and rough-set-theoretic to hybrid soft computing, with significant real-life applications. In addition, the book describes efficient soft machine learning algorithms for data mining and knowledge discovery. With a balanced mixture of theory, algorithms and applications, as well as up-to-date information and an extensive bibliography, Pattern Recognition: From Classical to Modern Approaches is a very useful resource.
