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E-raamat: Introduction to Pattern Recognition and Machine Learning

  • Formaat: EPUB+DRM
  • Ilmumisaeg: 09-Nov-2022
  • Kirjastus: Springer Nature Switzerland AG
  • Keel: eng
  • ISBN-13: 9783030959951
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Introduction to Pattern Recognition and Machine LearningSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 09-Nov-2022
  • Kirjastus: Springer Nature Switzerland AG
  • Keel: eng
  • ISBN-13: 9783030959951
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The domains of Pattern Recognition and Machine Learning have experienced exceptional interest and growth, however the overwhelming number of methods and applications can make the fields seem bewildering. This text offers an accessible and conceptually rich introduction, a solid mathematical development emphasizing simplicity and intuition. Students beginning to explore pattern recognition do not need a suite of mathematically advanced methods or complicated computational libraries to understand and appreciate pattern recognition; rather the fundamental concepts and insights, eminently teachable at the undergraduate level, motivate this text. This book provides methods of analysis that the reader can realistically undertake on their own, supported by real-world examples, case-studies, and worked numerical / computational studies.

Arvustused

The book is an introduction to pattern recognition and machine learning. ... The book brings a balance between the analytical and experimental approaches of teaching these important subjects. It offers a great deal of examples and case studies. ... The book contains very useful appendices for refreshing mathematical concepts. Overall, this is an excellent introduction to pattern recognition and machine learning. (Smaranda Belciug, zbMATH 1516.68002, 2023)

Preface v
Table of Contents
ix
List of Examples
xv
List of Algorithms
xvii
Notation xix
1 Overview
1(4)
2 Introduction to Pattern Recognition
5(24)
2.1 What Is Pattern Recognition?
5(4)
2.2 Measured Patterns
9(2)
2.3 Classes
11(3)
2.4 Classification
14(2)
2.5 Types of Classification Problems
16(3)
Case Study 2 Biometrics
19(4)
Numerical Lab 2 The Iris Dataset
23(4)
Further Reading
27(1)
Sample Problems
27(1)
References
28(1)
3 Learning
29(26)
Case Study 3 The Netflix Prize
44(2)
Numerical Lab 3 Overfitting and Underfitting
46(4)
Summary
50(1)
Further Reading
51(1)
Sample Problems
51(2)
References
53(2)
4 Representing Patterns
55(28)
4.1 Similarity
55(2)
4.2 Class Shape
57(16)
4.3 Cluster Synthesis
73(1)
Case Study 4 Defect Detection
74(2)
Numerical Lab 4 Working with Random Numbers
76(3)
Further Reading
79(1)
Sample Problems
79(3)
References
82(1)
5 Feature Extraction and Selection
83(34)
5.1 Fundamentals of Feature Extraction
83(10)
5.2 Feature Extraction and Selection
93(10)
Case Study 5 Image Searching
103(1)
Numerical Lab 5 Extracting Features and Plotting Classes
104(4)
Further Reading
108(2)
Sample Problems
110(4)
References
114(3)
6 Distance-Based Classification
117(34)
6.1 Definitions of Distance
118(6)
6.2 Class Prototype
124(8)
6.3 Distance-Based Classification
132(2)
6.4 Classifier Variations
134(4)
Case Study 6 Hand-writing Recognition
138(3)
Numerical Lab 6 Distance-Based Classifiers
141(2)
Further Reading
143(1)
Sample Problems
144(6)
References
150(1)
7 Inferring Class Models
151(42)
7.1 Parametric Estimation
152(2)
7.2 Parametric Model Learning
154(10)
7.3 Nonparametric Model Learning
164(10)
7.3.1 Histogram Estimation
165(3)
7.3.2 Kernel-Based Estimation
168(4)
7.3.3 Neighbourhood-based Estimation
172(2)
7.4 Distribution Assessment
174(5)
Case Study 7 Object Recognition
179(1)
Numerical Lab 7 Parametric and Nonparametric Estimation
180(3)
Further Reading
183(1)
Sample Problems
184(7)
References
191(2)
8 Statistics-Based Classification
193(38)
8.1 Non-Bayesian Classification: Maximum Likelihood
194(4)
8.2 Bayesian Classification: Maximum a Posteriori
198(3)
8.3 Statistical Classification for Normal Distributions
201(3)
8.4 Classification Error
204(7)
8.5 Other Statistical Classifiers
211(2)
Case Study 8 Medical Assessments
213(5)
Numerical Lab 8 Statistical and Distance-Based Classifiers
218(2)
Further Reading
220(1)
Sample Problems
221(9)
References
230(1)
9 Classifier Testing and Validation
231(36)
9.1 Working with Data
231(8)
9.2 Classifier Evaluation
239(10)
9.3 Classifier Validation
249(6)
Case Study 9 Autonomous Vehicles
255(2)
Numerical Lab 9 Leave-One-Out Validation
257(3)
Further Reading
260(1)
Sample Problems
260(5)
References
265(2)
10 Discriminant-Based Classification
267(32)
10.1 Linear Discriminants
269(2)
10.2 Discriminant Model Learning
271(9)
10.3 Nonlinear Discriminants
280(5)
10.4 Multi-Class Problems
285(3)
Case Study 10 Digital Communications
288(3)
Numerical Lab 10 Discriminants
291(3)
Further Reading
294(1)
Sample Problems
294(4)
References
298(1)
11 Ensemble Classification
299(48)
11.1 Combining Classifiers
301(4)
11.2 Resampling Strategies
305(7)
11.3 Sequential Strategies
312(7)
11.4 Nonlinear Strategies
319(13)
11.4.1 Neural Network learning
320(5)
11.4.2 Deep Neural Network Classifiers
325(7)
Case Study 11 Interpretability and Ethics of Large Networks
332(4)
Numerical Lab 11 Ensemble Classifiers
336(2)
Further Reading
338(1)
Sample Problems
339(5)
References
344(3)
12 Model-Free Classification
347(42)
12.1 Unsupervised Learning
348(22)
12.1.1 K-Means Clustering
351(7)
12.1.2 Kernel K-Means Clustering
358(5)
12.1.3 Mean-Shift Clustering
363(2)
12.1.4 Hierarchical Clustering
365(5)
12.2 Network-Based Clustering
370(3)
12.3 Semi-Supervised Learning
373(3)
Case Study 12 Ancient Text Analysis: Who Wrote What?
376(2)
Numerical Lab 12 Clustering
378(3)
Further Reading
381(1)
Sample Problems
381(6)
References
387(2)
13 Conclusions and Directions
389(6)
Appendices
395(72)
A Algebra Review
397(9)
Further Reading
404(1)
Sample Problems
405(1)
References
406(1)
B Random Variables and Random Vectors
407(17)
B.1 Random Variables
407(2)
B.2 Expectations
409(1)
B.3 Conditional Statistics
410(1)
B.4 Random Vectors and Covariances
411(5)
B.5 Outliers and Heavy-Tail Distributions
416(4)
B.6 Sample Statistics
420(2)
Further Reading
422(1)
Sample Problems
422(2)
References
424(3)
C Introduction to Optimization
427(9)
C.1 Basic Principles
427(1)
C.2 One-Dimensional Optimization
428(3)
C.3 Multi-Dimensional Optimization
431(3)
C.4 Multi-Objective Optimization
434(1)
Further Reading
435(1)
Sample Problems
436(1)
References
436(1)
D Mathematical Derivations
437(30)
Index 467
Paul Fieguth received the B.A.Sc. degree from the University of Waterloo, Canada, in 1991 and the Ph.D. degree from the Massachusetts Institute of Technology (MIT), United States, in 1995, both degrees in electrical engineering. He joined the faculty at the University of Waterloo in 1996, where he is currently Professor in Systems Design Engineering. He is a co-director of the Vision and Image Processing research group, where his research interests broadly involve machine learning for computer vision and statistical image processing. Specific interests include hierarchical algorithms for large problems, particularly in simplifying modelling and interpretation. In addition to this text, he is also the author on textbooks on Statistical Image Processing and Complex Systems.
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