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Market: Engineers and researchers in neural networks, image processing, audio/speech, and medical imaging. This book begins by focusing on the theoretical aspect of pattern recognition and introduces an integrated pattern recognition paradigm, which combines preprocessing, low dimensional signal characterization, feature optimization, and mapping classifier architecture to good features in a seamless fashion. Later, the authors reinforce the concepts of pattern recognition and prediction with challenging real- world examples, encompassing financial market prediction, image coding, active and passive sonar processing, chaos modeling, and intelligent product design.

Pattern Recognition and Prediction with Applications to Signal Processing

Contents: 1. Introduction. 2. Data Projection. 3. Feature Extraction and Optimization. 4. Classifier Architecture. 5. Hidden Markov Models. 6. Hierarchical and Hybrid Classification. 7. Pattern Recognition Summary. 8. Bioreject. 9. Reduced Dimension Image Compression. 10. Full Spectrum Signal Processing. 11. LFA Echo Characterization. 12. Nonlinear System Modeling with HMM. 13. Prediction and Trend Analysis. 14. Knowledge Creation in Business.