{"product_id":"a-statistical-approach-to-neural-networks-for-pattern-recognition-9780471741084","title":"A Statistical Approach to Neural Networks for Pattern Recognition","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book presents a statistical treatment of the Multilayer Perceptron (MLP), which is the most widely used of the neural network models, in a language that is familiar to practicing statisticians. Questions arise when statisticians are first confronted with such a model, and this book's aim is to provide thorough answers.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"This book is a good introduction to neural networks for a statistician.\" (\u003ci\u003eJournal of the American Statistical Association\u003c\/i\u003e, March 2009)  \u003cp\u003e\"The book provides an excellent introduction to neutral networks from a statistical perspective.\" (\u003ci\u003eInternational Statistical Review\u003c\/i\u003e, 2008)\u003c\/p\u003e \u003cp\u003e\"Successful connects logistic regression and linear discriminant analysis, thus making it critical reference and self-study guide for students and professionals alike in the fields of mathematics, statistics, computer science, and electrical engineering.\" (\u003ci\u003eMathematical Reviews\u003c\/i\u003e)\u003cbr\u003e \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eNotation and Code Examples.  \u003cp\u003ePreface.\u003c\/p\u003e \u003cp\u003eAcknowledgments.\u003c\/p\u003e \u003cp\u003e1. Introduction.\u003c\/p\u003e \u003cp\u003e2. The Multi-Layer Perception Model.\u003c\/p\u003e \u003cp\u003e3. Linear Discriminant Analysis.\u003c\/p\u003e \u003cp\u003e4. Activation and Penalty Functions.\u003c\/p\u003e \u003cp\u003e5. Model Fitting and Evaluation.\u003c\/p\u003e \u003cp\u003e6. The Task-Based MLP.\u003c\/p\u003e \u003cp\u003e7. Incorporating Spatial Information into an MLP Classifier.\u003c\/p\u003e \u003cp\u003e8. Influence Curves for the Multi-Layer Perceptron Classifier.\u003c\/p\u003e \u003cp\u003e9. The Sensitivity Curves of the MLP Classifier.\u003c\/p\u003e \u003cp\u003e10. A Robust Fitting Procedure for MLP Models.\u003c\/p\u003e \u003cp\u003e11. Smoothed Weights.\u003c\/p\u003e \u003cp\u003e12. Translation Invariance.\u003c\/p\u003e \u003cp\u003e13. Fixed-slope Training.\u003c\/p\u003e \u003cp\u003eAppendix A. Function Minimization.\u003c\/p\u003e \u003cp\u003eAppendix B. Maximum Values of the Influence Curve.\u003c\/p\u003e \u003cp\u003eTopic Index.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":51767551951191,"sku":"9780471741084","price":105.26,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471741084.jpg?v=1758713753","url":"https:\/\/bookcurl.com\/products\/a-statistical-approach-to-neural-networks-for-pattern-recognition-9780471741084","provider":"Book Curl","version":"1.0","type":"link"}