Description
Book SynopsisIntelligent signal processing (ISP) differs fundamentally from the classical approach to statistical signal processing in that the input--output behavior of a complex system is modeled by using an artificial intelligence capable of optimizing results.
Table of ContentsPreface.
List of Contributors.
Humanistic Intelligence: "Wear Comp" As a New Framework and Application for Intelligent Signal Processing.
Adaptive Stochastic Resonance.
Learning in the Presence of Noise.
Incorporating Prior Information in Machine Learning by Creating Virtual Examples.
Deterministic Annealing for Clustering, Compression, Classification, Regression, and Speech recognition.
Local Dynamic Modeling with Self-Organizing Maps and Applications to Nonlinear System Identification and Control.
A Signal Processing Framework Based on Dynamic Neural Networks with Application to Problems in Adaptation, Filtering and Classification.
Semiparametric Support Vector Machines for Nonlinear Model Estimation.
Gradient-Based Learning Applied to Document Recognition.
Pattern Recognition Using A Family of Design Algorithms Based Upon Generalized Probabilistic Descent Method.
An Approach to Adaptive Classification.
Reduced-Rank Intelligent Signal Processing with Application to Radar.
Signal Detection in a Nonstationary Environment Reformulated as an Adaptive Pattern Classification Problem.
Data Representation Using Mixtures of Principal Components.
Image Denoising by Sparse Code Shrinkage.
Index.
About the Editors.
