Pattern Classification
The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics.
An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.
Why Read This Book
You should read this book because it gives a clear, mathematically grounded introduction to statistical pattern recognition and early neural-network methods, tying theory to worked examples and projects. You will gain the conceptual tools to design, analyze, and compare classifiers used in signal-processing problems such as speech/audio recognition, radar detection, and communications demodulation.
Who Will Benefit
Engineers and graduate students working on signal-processing problems that require classification, detection, or supervised learning—especially those translating statistical theory into practical DSP systems.
Level: Intermediate — Prerequisites: Undergraduate probability and statistics, linear algebra, and calculus; familiarity with basic signal-processing concepts and some programming for experiments.
Key Takeaways
- Understand Bayesian decision theory and how to derive optimal classifiers under different cost/prior assumptions.
- Implement and compare parametric and nonparametric classifiers (Gaussian discriminants, k-NN, Parzen windows) and assess their trade-offs.
- Design and evaluate feature-extraction and dimensionality-reduction strategies to improve classifier performance on real signals.
- Apply expectation-maximization and mixture models for density estimation and unsupervised structure discovery.
- Use neural-network classifiers and understand their relation to classical statistical methods.
- Estimate classifier performance reliably through error estimation, cross-validation, and bias/variance considerations.
Topics Covered
- 1. Introduction and Background
- 2. Bayesian Decision Theory and Classification
- 3. Classifiers Based on the Normal Density (Linear/Quadratic Discriminants)
- 4. Nonparametric Techniques: k-NN and Parzen Windows
- 5. Density Estimation and Mixture Models (EM Algorithm)
- 6. Linear Feature Extraction and Dimensionality Reduction
- 7. Neural Networks and Practical Learning Algorithms
- 8. Clustering and Unsupervised Learning
- 9. Error Estimation, Model Selection, and Cross-Validation
- 10. Invariances, Representations, and Practical Considerations
- Appendices: Mathematical Background and Worked Examples
Languages, Platforms & Tools
How It Compares
Covers the same foundational territory as Bishop's 'Pattern Recognition and Machine Learning' and Hastie/Tibshirani/Friedman's 'The Elements of Statistical Learning' but is older and more concise—Duda et al. is especially strong on classical decision theory and intuitive examples, while Bishop/ESL offer more modern Bayesian treatments and broader, up-to-date ML coverage.
