SD675 – Pattern Recognition References. Topic. References. Background. 372
Notes — Ch. 2. Duda, Hart, Stork — Appen. A. Introduction. 372 Notes — Ch. 1.
SD675 – Pattern Recognition References Topic
References
Background
372 Notes — Ch. 2 Duda, Hart, Stork — Appen. A
Introduction
372 Notes — Ch. 1 Duda, Hart, Stork — Ch. 1
Basic Classification Statistical Classification
372 Notes — Ch. 4 Duda, Hart, Stork — Ch. 2 Schalkoff — Ch. 2
Parametric Classification
372 Notes — Ch. 3
Nonparametric Classification
372 Notes — Ch. 3.2 Duda, Hart, Stork — Ch. 4.5
Linear Discriminants
372 Notes — Ch. 6 Duda, Hart, Stork — Ch. 5
Classifier Assessment No Free Lunch, Occam Validation, m-Valid Leave-one-out
Duda, Hart, Stork — Ch. 9.2 Duda, Hart, Stork — Ch. 9.6
Classification Error
372 Notes — Ch. 4.3 Duda, Hart, Stork — Ch. 2.7
Error Bounds
Duda, Hart, Stork — Ch. 2.8 Shanmugan & Breipohl — Ch. 2.7
Hypothesis Testing Overview
Neyman Pearson
372 Notes — Ch. 4 Duda, Hart, Stork — Ch. 2.8, 2.9 Schalkoff — Ch. 2 Duda, Hart, Stork — Ch. 2.3
Generalized Hypoth. Testing
Shanmugan & Breipohl — Ch. 6, 8.8 Papoulis — Ch. 9.3
Statistics (Chi, PCP) Classifier Generalization:
Papoulis — Ch. 9.3 (Page 273) Shanmugan & Breipohl — Ch. 5.4
Boosting, Bagging
Duda, Hart, Stork — Ch. 9.5
Voting Schemes
Duda, Hart, Stork — Ch. 9.7
SVMs
Duda, Hart, Stork — Ch. 5.11
Neural
Duda, Hart, Stork — Ch. 5.5, 6 Haykin Schalkoff — Ch. 11,12
Parameter Estimation Bayesian Estimation
372 Notes — Ch. 5.1 Duda, Hart, Stork — Ch. 3.3-3.5 Papoulis — Ch. 9.2 Schalkoff — Ch. 3 (Pages 58–65) Shanmugan & Breipohl — Ch. 8.4, 8.5
Maximum Likelihood
372 Notes — Ch. 5.1 Duda, Hart, Stork — Ch. 3.2
Cramer Rao Bound
Papoulis — Ch. 9.2 (Page 263) Wetherill, Sect. 3.5, QA276.W455
EM
Duda, Hart, Stork — Ch. 3.9
NonParametric Estimation Histogram
372 Notes — Ch. 5.2
Parzen
Duda, Hart, Stork — Ch. 4.3 Schalkoff — Ch. 3 (Pages 66–75) Shanmugan & Breipohl — Ch. 8.3
kNN
Duda, Hart, Stork — Ch. 4.4
Estimator Validation
Jackknife Delete-d Jack Bootstrapping
Duda, Hart, Stork — Ch. 9.4, 9.6
Feature Extraction Principle Comp.
Duda, Hart, Stork — Ch. 10.13
Fisher’s Lin. Disc.
372 Notes — Ch. 8 Duda, Hart, Stork — Ch. 3.8
Feature Extraction Feature Selection
372 Notes — Ch. 8 Meisel — Ch. 9
Other Topics Information Theory
372 Notes — Ch. 8 Duda, Hart, Stork — Appen. A Gallager
Unlabeled Clustering
372 Notes — Ch. 7 Duda, Hart, Stork — Ch. 10 Schalkoff — Ch. 5
Syntax, Grammars
Duda, Hart, Stork — Ch. 8.5, 8.6, 8.7 Schalkoff — Ch. 6
Finite-State Automata
Nadler & Smith — Ch. 9
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Duda, Hart, Stork
Q327.D83
Fukunaga Gallager Haykin Meisel
Q327.F85 Q360.G3 QA76.87.H39 Q327.M44
Pattern classification and scene analysis
Introduction to Statistical Pattern Recognition Information theory and reliable communication Neural networks : a comprehensive foundation Computer-Oriented Approaches to Pattern Recognition Nadler & Smith TK7882.P3N3 Pattern recognition engineering Papoulis QA273.P2 Probability, random variables, and stochastic processes Schalkoff Q327.S27 Pattern recognition : statistical, structural, and neural approaches Shanmugan & Breipohl TK5102.5.S447 Random signals : detection, estimation, and data analysis
# - Material held on reserve in the Davis Centre library (under SYDE 372)
