NeuralNetworksforPatternRecognition

This book offers a comprehensive treatment of feed-forward neural networks, focusing on statistical pattern recognition. It starts by introducing the fundamental concepts of pattern recognition and then proceeds to describe techniques for modelling probability density functions. Additionally, the book considers the properties and merits of both the multi-layer perceptron and radial basis functions.

Overall, this book provides valuable insights into the application of neural networks in the field of statistical pattern recognition.

作者简介

Thisbookprovidesthefirstcomprehensivetreatmentoffeed-forwardneuralnetworksfromtheperspectiveofstatisticalpatternrecognition.Afterintroducingthebasicconceptsofpatternrecognition,thebookdescribestechniquesformodellingprobabilitydensityfunctions,anddiscussesthepropertiesandrelativemeritsofthemulti-layerperceptronandradialbasisfunctionnetworkmodels.Italsomotivatestheuseofvariousformsoferrorfunctions,andreviewstheprincipalalgorithmsforerrorfunctionminimization.Aswellasprovidingadetaileddiscussionoflearningandgeneralizationinneuralnetworks,thebookalsocoverstheimportanttopicsofdataprocessing,featureextraction,andpriorknowledge.ThebookconcludeswithanextensivetreatmentofBayesiantechniquesandtheirapplicationstoneuralnetworks.