Statistical Pattern Recognition
Statistical pattern recognition is a very active area of study and research, which has seen many advances in recent years. New and emerging applications - such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition - require robust and efficient pattern recognition techniques. Statistical decision making and estimation are regarded as fundamental to the study of pattern recognition.
Statistical Pattern Recognition, Second Edition has been fully updated with new methods, applications and references. It provides a comprehensive introduction to this vibrant area - with material drawn from engineering, statistics, computer science and the social sciences - and covers many application areas, such as database design, artificial neural networks, and decision support systems.
* Provides a self-contained introduction to statistical pattern recognition.
* Each technique described is illustrated by real examples.
* Covers Bayesian methods, neural networks, support vector machines, and unsupervised classification.
* Each section concludes with a description of the applications that have been addressed and with further developments of the theory.
* Includes background material on dissimilarity, parameter estimation, data, linear algebra and probability.
* Features a variety of exercises, from 'open-book' questions to more lengthy projects.
The book is aimed primarily at senior undergraduate and graduate students studying statistical pattern recognition, pattern processing, neural networks, and data mining, in both statistics and engineering departments. It is also an excellent source of reference for technical professionals working in advanced information development environments.
For further information on the techniques and applications discussed in this book please visit www.statistical-pattern-recognition.net
Why Read This Book
You should read this book if you need a clear, statistically principled foundation for classification and estimation methods used in signal processing tasks. It gives you practical derivations and intuition for Bayesian decision theory, density estimation, mixture models and common classifier evaluation techniques so you can design and assess pattern-recognition pipelines for signals.
Who Will Benefit
Graduate students and practicing engineers working on classification, detection, or feature extraction for audio, speech, radar or communications who need a solid statistical basis for design and evaluation.
Level: Intermediate — Prerequisites: Undergraduate-level probability and statistics, linear algebra and calculus; familiarity with basic programming or MATLAB/NumPy will help for implementing examples.
Key Takeaways
- Apply Bayesian decision theory to derive optimal classifiers under different loss functions and priors.
- Estimate model parameters for common parametric models (e.g., Gaussian models) and use mixture models with the EM algorithm.
- Implement and evaluate nonparametric density estimators and nearest-neighbor classifiers for real data.
- Perform dimensionality reduction and feature selection using PCA, LDA and related techniques to improve classifier performance.
- Use clustering techniques and model selection criteria to discover structure in unlabeled signal data.
- Assess classifier performance with ROC analysis, cross-validation and error-cost tradeoffs.
Topics Covered
- Introduction and overview of pattern recognition applications
- Probability and statistical inference foundations
- Bayesian decision theory and discriminant functions
- Parameter estimation and maximum likelihood methods
- Linear and quadratic discriminant analysis
- Nonparametric methods and density estimation (k-NN, Parzen)
- Mixture models and the EM algorithm
- Dimensionality reduction and feature selection (PCA, LDA)
- Clustering methods and unsupervised learning
- Model selection, validation and performance evaluation
- Sequential models and simple temporal models
- Practical considerations and applications (case studies)
- Mathematical appendices and further reading
How It Compares
Covers similar statistical foundations as Duda, Hart & Stork's Pattern Classification but is more concise and focused on modern statistical estimation/mixture approaches; it is less tutorial-driven than Bishop's Pattern Recognition and Machine Learning but more accessible for engineers than pure theoretical texts.
