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Textbook on Pattern Recognition [Kõva köide]

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  • Formaat: Hardback, 122 pages, kõrgus x laius: 240x160 mm, kaal: 360 g
  • Ilmumisaeg: 30-Jan-2015
  • Kirjastus: Alpha Science International Ltd
  • ISBN-10: 1842658409
  • ISBN-13: 9781842658406
Teised raamatud teemal:
Textbook on Pattern RecognitionSuurem pilt
  • Formaat: Hardback, 122 pages, kõrgus x laius: 240x160 mm, kaal: 360 g
  • Ilmumisaeg: 30-Jan-2015
  • Kirjastus: Alpha Science International Ltd
  • ISBN-10: 1842658409
  • ISBN-13: 9781842658406
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781842658406.html
Presents both classical and current theoretical foundation of pattern recognition and practice of supervised, unsupervised and reinforces learning to build a complete background of professionals and engineering students.

Every pattern of the image and design cycles has been discussed in detail through illustrative examples and applications. The very latest method are incorporated in this text book including k-mean clustering, k-nearest neighborhood methods etc.

• Coverage of Learning Adaptation.
• Design Cycle of Pattern Recognition.
• Algorithms have been presented in step by step in illustration of the same in problems.
• Probability distribution function with Cumulative and Uniform PDF methods
• Coverage of Optical Character recognition, face recognition, and speech recognition and image analysis.

The information is clearly presented and illustrated by many examples and applications and is designed for use as a text for course in pattern recognition for both undergraduate and post graduate engineering students.
Preface v
1 Pattern Recognition
1(1)
1.1 Introduction
1(1)
1.2 Basics of Pattern Recognition
2(2)
1.2.1 Pattern
2(2)
1.2.2 Recognition
4(1)
1.3 Pattern Recognition Applications
4(4)
1.3.1 Speech Recognition
4(1)
1.3.2 Optical Character Recognition
5(1)
1.3.3 Fingerprint Identification
6(2)
1.4 Pattern Recognition Structure
8(1)
1.4.1 Sensor
8(1)
1.4.2 Segmentation
8(1)
1.4.3 Feature Extraction
9(1)
1.4.4 Classification
9(1)
1.5 Design Principles of Pattern Recognition
9(1)
1.6 The Design Cycle of Pattern Recognition
10(2)
1.6.1 Data Collection
11(1)
1.6.2 Feature Selection
11(1)
1.6.3 Model Selection
11(1)
1.6.4 Training Classifier
12(1)
1.6.5 Evaluation Classifier
12(1)
1.7 Learning
12(3)
1.7.1 Supervised Learning
13(1)
1.7.2 Unsupervised Learning
14(1)
1.7.3 Reinforcement Learning
14(1)
1.8 Adaptation
15(1)
1.9 Basic Pattern Recognition Approaches
15
1.9.1 Statistical Pattern Recognition
16(1)
1.9.2 Neural Pattern Recognition
17(1)
1.9.3 Syntactical Pattern Recognition
18(1)
Summary
19(1)
Exercises
20
2 Mathematical Foundations
1(1)
2.1 Introduction to Linear Algebra
1(1)
2.1.1 Vector Space
2(1)
2.2 Probability theory
2(3)
2.2.1 Set Definitions
2(1)
2.2.2 Set Notation
3(1)
2.2.3 Set Operations
3(1)
2.2.4 Probability
3(2)
2.3 Important Properties of Probabilities
5(4)
2.3.1 Join Probability
5(1)
2.3.2 Conditional Probability
5(2)
2.3.3 Total Probability
7(2)
2.4 Estimation
9(1)
2.4.1 Point Estimate
9(1)
2.4.2 Interval Estimate
9(1)
2.5 Mean
9(2)
2.5.1 Arithmetic Mean (AM)
10(1)
2.5.2 Geometric Mean (GM)
10(1)
2.5.3 Harmonic Mean (HM)
11(1)
2.6 Variance
11(1)
2.7 Probability Distribution Function
12(1)
2.7.1 Cumulative Probability Distributions Function
12(1)
2.7.2 Uniform Probability Distribution Function
13(1)
2.8 Classification of Probability Distribution Function
13(5)
2.8.1 Discrete Probability Distributions
13(1)
2.8.2 Continuous Probability Distributions
14(4)
2.9 Chi-Square Test
18
2.9.1 Chi-Square Distribution Table
19(1)
Summary
20(1)
Exercises
20
3 Statistical Pattern Recognition
1(1)
3.1 Introduction
1(1)
3.2 Bayes Decision Theory
2
Summary
12(1)
Exercises
12
4 Parameter Estimation Methods
1(1)
4.1 Introduction
1(1)
4.2 Maximum Likelihood Estimation
2(3)
4.3 Bayesian Estimation
5(3)
4.4 Dimension Reduction
8(1)
4.5 Principal Components Analysis
9(1)
4.6 Computing Process of Principal Components
10(1)
4.7 Fisher Linear Discriminate Analysis
11
Summary
13(1)
Exercises
14
5 Expectation Maximization
1(1)
5.1 Introduction
1(2)
5.2 Hidden Markov Model
3(4)
5.3 Gaussian Mixture Model
7
5.3.1 Mixture Model
9(1)
Summary
10(1)
Exercises
10
6 Non-parametric Techniques of Estimation
1(1)
6.1 Density Estimation
1(5)
6.2 K-Nearest Neighbor Rule
6(2)
6.3 Nearest Neighbor Rules
8
Summary
15(1)
Exercises
16
7 Unsupervised Learning and Clustering
1(1)
7.1 Introduction
1(1)
7.2 Clustering
1(2)
7.2.1 Basic Step of Clustering
2(1)
7.3 Clustering Techniques
3(2)
7.3.1 Partitioning Clustering
3(1)
7.3.2 Hierarchical Clustering
3(1)
7.3.3 Agglomerative Hierarchical Clustering
4(1)
7.3.4 Divisive Hierarchical Clustering
5(1)
7.4 Linkage Method of Hierarchical Clustering
5(2)
7.4.1 Average Linkage Method
5(1)
7.4.2 Centroid Linkage Method
6(1)
7.4.3 Complete Linkage Method
6(1)
7.4.4 Single Linkage Method
7(1)
7.5 K-Mean Clustering
7(5)
7.6 Cluster Validation
12
7.6.1 Measuring Approaches of Cluster Validation
13(1)
Summary
14(1)
Exercises
14
Index 1