Supervised and Unsupervised Pattern Recognition Feature Extraction and Computational Intelligence

This volume describes the application of a novel, unsupervised pattern recognition scheme to the classification of various types of waveforms and images. It begins with an introduction to Neural Networks (NNs), classifiers, and feature extraction methods, then addresses unsupervised neural networks and fuzzy neural networks and their applications to handwritten character recognition and recognition of normal and abnormal visual evoked potentials. The third section deals with advanced neural network architectures, including modular design and their applications to medicine and three-dimensional NN architecture simulating brain functions. The final section discusses general applications and simulations in various fields. TOC:Overviews of Neural Networks, Classifiers, and Feature Extraction Methods - Supervised Neural Networks.- Unsupervised Neural Networks.- Advanced Neural Network Architectures/Modular Neural Networks.- General Applications.

There are many books on neural networks, some of which cover computational intelligence, but none that incorporate both feature extraction and computational intelligence, as Supervised and Unsupervised Pattern Recognition does. This volume describes the application of a novel, unsupervised pattern recognition scheme to the classification of various types of waveforms and images. This substantial collection of recent research begins with an introduction to Neural Networks, classifiers, and feature extraction methods. It then addresses unsupervised and fuzzy neural networks and their applications to handwritten character recognition and recognition of normal and abnormal visual evoked potentials. The third section deals with advanced neural network architectures-including modular design-and their applications to medicine and three-dimensional NN architecture simulating brain functions. The final section discusses general applications and simulations, such as the establishment of a brain-computer link, speaker identification, and face recognition.In the quickly changing field of computational intelligence, every discovery is significant. Supervised and Unsupervised Pattern Recognition gives you access to many notable findings in one convenient volume.

classifiers-an overviewCriteria for optimal classifier designCategorizing the ClassifiersClassifiersNeural NetworksComparison of Experimental ResultsSystem Performance AssessmentAnalysis of Prediction Rates from Bootstrapping AssessmentARTIFICIAL NEURAL NETWORKS: DEFINITIONS, METHODS, APPLICATIONSDefinitionsTraining AlgorithmSome ApplicationsA SYSTEM FOR HANDWRITTEN DIGIT RECOGNITIONPreprocessing of Handwritten Digit ImagesZernike Moments (ZM) for Characterization of Image PatternsDimensionality ReductionAnalysis of Prediction Error Rates from Bootstrapping AssessmentSummary OTHER TYPES OF FEATURE EXTRACTION METHODSIntroductionWaveletsInvariant MomentsEntropyCepstrum Analysis Fractal DimensionEntropySGLD Texture FeaturesFUZZY NEURAL NETWORKSPattern RecognitionOptimizationSystem DesignClusteringAPPLICATION TO HANDWRITTEN DIGITSIntroduction to Character RecognitionData CollectionResultsDiscussionSummary A UNSUPERVISED NEURAL NETWORK SYSTEM FOR VISUAL EVOKED POTENTIALSData Collection and PreprocessingSystem DesignResultsDiscussion CLASSIFICATION OF MAMMOGRAMS USING A MODULAR NEURAL NETWORKMethods and System OverviewModular Neural NetworksNeural Network TrainingClassification ResultsThe Process of Obtaining ResultsALOPEX ParametersGeneralizationConclusions"VISUAL OPHTHALMOLOGIST": AN AUTOMATED SYSTEM FOR CLASSIFICATION OF RETINAL DAMAGESystem OverviewModular Neural NetworksApplications to OphthalmologyResultsDiscussionA THREE-DIMENSIONAL NEURAL NETWORK ARCHITECTUREThe Neural Network ArchitectureSimulationsDiscussionA FEATURE EXTRACTION ALGORITHM USING CONNECTIVITY STRENGTHS AND MOMENT INVARIANTSALOPEX AlgorithmsMoment Invariants and ALOPEXResults and DiscussionMULTILAYER PERCEPTRONS WITH ALOPEX: 2D-TEMPLATE MATCHING AND VLSI IMPLEMENTATIONMultilayer Perceptron and Template MatchingVLSI Implementation of ALOPEXIMPLEMENTING NEURAL NETWORKS IN SILICONThe Living NeuronNeuromorphic ModelsNeurological Process ModelingSPEAKER IDENTIFICATION THROUGH WAVELET MULTIRESOLUTION DECOMPOSITION AND ALOPEXMultiresolution Analysis through Wavelet DecompositionPattern Recognition with ALOPEXMethodsResultsDiscussionFACE RECOGNITION IN ALZHEIMER'S DISEASE: A SIMULATIONMethodsResultsDiscussionSELF-LEARNING LAYERED NEURAL NETWORKSNeocognition and Pattern ClassificationObjectivesMethodsStudy AStudy BSummary and DiscussionBIOLOGICAL AND MACHINE VISIONDistributed RepresentationThe ModelA Modified ALOPEX AlgorithmApplication to Template MatchingBrain-to-Computer LinkDiscussionEach section also has an introduction and references

Evangelia Miche Tzanakou