Supervised and Unsupervised Pattern Recognition

"This book is an excellent source of knowledge of state-of-the-art feature extraction…Supervised and unsupervised learning and training schemes are notable finds…Exciting applications of signal and image analysis and recognition…This book provides in-depth guidance and inspiring ideas to new applications of signal and image analysis and recognition."--Tonglei Li, Ph.D., Purdue University, School of Pharmacy"…great efforts have been made in a number of communities to explore solutions to pattern recognition problems…this book describes their efforts made over ten researchers in the Neuroelectric and Neurocomputing Laboratories at Rutgers University. Along with concise introductory materials in pattern recognition, this volume presents several applications of supervised and unsupervised schemes to the classification of various types of signals and images…Unlike other books in neural networks, this book gives an emphasis on feature extraction as well, which provides a systematic way to deal with pattern recognition problems in terms of neural networks and computational intelligence…it is worth noting that each chapter contains an extensive bibliography that provides a reliable list of good references. We believe that readers will find this list very useful to understand the materials in the book and cautious beginners in the related fields might benefit from this list as well…helpful to a broad audience of graduate students, researchers, practicing engineers and professionals in computer and information science, electrical engineering, and biomedical informatics…this book reflects the long-term continuous endeavors of a research group for conducting innovatory researches, which could provide some useful hints to those novices in related fields…pioneering volume…welcomed by all interested in the fields of pattern recognition and computational intelligence…the editor's serious attempt to address the aforementioned issue must be welcomed by all interested in the fields of pattern recognition and computational intelligence and, therefore, this book deserves all credit."--Ke Chen, National Laboratory of Machine Perception and The Center for Information Science, Peking University, Beijing, ChinaPromo Copy

• Classifiers-an overview, Criteria for optimal classifier design, Categorizing the Classifiers, Classifiers, Neural Networks, Comparison of Experimental Results, System Performance Assessment, Analysis of Prediction Rates from Bootstrapping Assessment, ARTIFICIAL NEURAL NETWORKS: DEFINITIONS, METHODS, APPLICATIONS, Definitions, Training Algorithm, Some Applications, A SYSTEM FOR HANDWRITTEN DIGIT RECOGNITION, Preprocessing of Handwritten Digit Images, Zernike Moments (ZM) for Characterization of Image Patterns, Dimensionality Reduction, Analysis of Prediction Error Rates from Bootstrapping Assessment, Summary , OTHER TYPES OF FEATURE EXTRACTION METHODS, Introduction, Wavelets, Invariant Moments, Entropy, Cepstrum Analysis , Fractal Dimension, Entropy, SGLD Texture Features, FUZZY NEURAL NETWORKS, Pattern Recognition, Optimization, System Design, Clustering, APPLICATION TO HANDWRITTEN DIGITS, Introduction to Character Recognition, Data Collection, Results, Discussion, Summary , A UNSUPERVISED NEURAL NETWORK SYSTEM FOR VISUAL EVOKED POTENTIALS, Data Collection and Preprocessing, System Design, Results, Discussion , CLASSIFICATION OF MAMMOGRAMS USING A MODULAR NEURAL NETWORK, Methods and System Overview, Modular Neural Networks, Neural Network Training, Classification Results, The Process of Obtaining Results, ALOPEX Parameters, Generalization, Conclusions, VISUAL OPHTHALMOLOGIST: AN AUTOMATED SYSTEM FOR CLASSIFICATION OF RETINAL DAMAGE, System Overview, Modular Neural Networks, Applications to Ophthalmology, Results, Discussion, A THREE-DIMENSIONAL NEURAL NETWORK ARCHITECTURE, The Neural Network Architecture, Simulations, Discussion, A FEATURE EXTRACTION ALGORITHM USING CONNECTIVITY STRENGTHS AND MOMENT INVARIANTS, ALOPEX Algorithms, Moment Invariants and ALOPEX, Results and Discussion, MULTILAYER PERCEPTRONS WITH ALOPEX: 2D-TEMPLATE MAT