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Pattern Recognition : An Algorithmic Approach / by M. Narasimha Murty, V. Susheela Devi
Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Undergraduate Topics in Computer Science ; 0 | SpringerLink BücherPublisher: London : Universities Press (India) Pvt. Ltd, 2011Edition: 1Description: Online-Ressource (XII, 263p, digital)ISBN:- 9780857294951
- 006.4
- QA75.5-76.95
- TK7882.P3
Contents:
Summary: Observing the environment and recognising patterns for the purpose of decision making is fundamental to human nature. This book deals with the scientific discipline that enables similar perception in machines through pattern recognition (PR), which has application in diverse technology areas. This book is an exposition of principal topics in PR using an algorithmic approach. It provides a thorough introduction to the concepts of PR and a systematic account of the major topics in PR besides reviewing the vast progress made in the field in recent times. It includes basic techniques of PR, neural networks, support vector machines and decision trees. While theoretical aspects have been given due coverage, the emphasis is more on the practical. The book is replete with examples and illustrations and includes chapter-end exercises. It is designed to meet the needs of senior undergraduate and postgraduate students of computer science and allied disciplines. Dr. M. Narasimha Murty is a professor in the Department of Computer Science and Automation at the Indian Institute of Science, Bangalore. Dr. V. Susheela Devi is a senior scientific officer in the Department of Computer Science and Automation at the Indian Institute of Science, Bangalore.PPN: PPN: 1650946546Package identifier: Produktsigel: ZDB-2-SCS
""Contents""; ""Preface""; ""1 Introduction ""; ""1.1 What is Pattern Recognition?""; ""1.2 Data Sets for Pattern Recognition""; ""1.3 Different Paradigms for Pattern Recognition""; ""Discussion""; ""Further Reading""; ""Exercises""; ""Bibliography""; ""2 Representation ""; ""2.1 Data Structures for Pattern Representation""; ""2.1.1 Patterns as Vectors""; ""2.1.2 Patterns as Strings""; ""2.1.3 Logical Descriptions""; ""2.1.4 Fuzzy and Rough Pattern Sets""; ""2.1.5 Patterns as Trees and Graphs""; ""2.2 Representation of Clusters""; ""2.3 Proximity Measures""; ""2.3.1 Distance Measure""
""2.3.2 Weighted Distance Measure""""2.3.3 Non-Metric Similarity Function""; ""2.3.4 Edit Distance""; ""2.3.5 Mutual Neighbourhood Distance (MND)""; ""2.3.6 Conceptual Cohesiveness""; ""2.3.7 Kernel Functions""; ""2.4 Size of Patterns""; ""2.4.1 Normalisation of Data""; ""2.4.2 Use of Appropriate Similarity Measures""; ""2.5 Abstractions of the Data Set""; ""2.6 Feature Extraction""; ""2.6.1 Fisher�s Linear Discriminant""; ""2.6.2 Principal Component Analysis (PCA)""; ""2.7 Feature Selection""; ""2.7.1 Exhaustive Search""; ""2.7.2 Branch and Bound Search""
""2.7.3 Selection of Best Individual Features""""2.7.4 Sequential Selection""; ""Sequential Forward Selection (SFS)""; ""Sequential Backward Selection (SBS)""; ""2.7.5 Sequential Floating Search""; ""Sequential Floating Forward Search (SFFS)""; ""Sequential Floating Backward Search (SBFS)""; ""2.7.6 Max�Min Approach to Feature Selection""; ""2.7.7 Stochastic Search Techniques""; ""2.7.8 Artificial Neural Networks""; ""2.8 Evaluation of Classifiers""; ""2.9 Evaluation of Clustering""; ""Discussion""; ""Further Reading""; ""Exercises""; ""Computer Exercises""; ""Bibliography""
""3 Nearest Neighbour Based Classifiers """"3.1 Nearest Neighbour Algorithm""; ""3.2 Variants of the NN Algorithm""; ""3.2.1 k-Nearest Neighbour (kNN) Algorithm""; ""3.2.2 Modified k-Nearest Neighbour (MkNN) Algorithm""; ""3.2.3 Fuzzy kNN Algorithm""; ""3.2.4 r Near Neighbours""; ""3.3 Use of the Nearest Neighbour Algorithm for Transaction Databases""; ""3.4 Efficient Algorithms""; ""3.4.1 The Branch and Bound Algorithm""; ""3.4.2 The Cube Algorithm""; ""3.4.3 Searching for the Nearest Neighbour by Projection""; ""3.4.4 Ordered Partitions""; ""3.4.5 Incremental Nearest Neighbour Search""
""3.5 Data Reduction""""3.6 Prototype Selection""; ""3.6.1 Minimal Distance Classifier (MDC)""; ""3.6.2 Condensation Algorithms""; ""Condensed Nearest Neighbour Algorithm""; ""Modified Condensed Nearest Neighbour Algorithm""; ""3.6.3 Editing Algorithms""; ""3.6.4 Clustering Methods""; ""3.6.5 Other Methods""; ""Discussion""; ""Further Reading""; ""Exercises""; ""Computer Exercises""; ""Bibliography""; ""4 Bayes Classifier ""; ""4.1 Bayes Theorem""; ""4.2 Minimum Error Rate Classifier""; ""4.3 Estimation of Probabilities""; ""4.4 Comparison with the NNC""; ""4.5 Naive Bayes Classifier""
""4.5.1 Classification using Naive Bayes Classifier""
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