{"product_id":"9783540306764","title":"Studies in Computational Intelligence","description":"\u003ch1\u003eStudies in Computational Intelligence\u003c\/h1\u003e \u003ch2\u003eJin, Yaochu\u003c\/h2\u003e \u003cp\u003e\u003c\/p\u003e\u003cp\u003eRecently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems. \u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Springer\u003c\/p\u003e \u003cp\u003ePublication Date: 2006-02-10\u003c\/p\u003e \u003cp\u003eFormat: Hardcover\u003c\/p\u003e \u003cp\u003eISBN-13: 9783540306764\u003c\/p\u003e \u003cp\u003eDOI: 10.1007\/3-540-33019-4\u003c\/p\u003e \u003cp\u003eDimensions: 235cm x155cm\u003c\/p\u003e \u003cp\u003ePages: 660\u003c\/p\u003e ","brand":"Springer Berlin Heidelberg","offers":[{"title":"Default Title","offer_id":45378537554060,"sku":"9783540306764","price":197.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9783540306764.jpg?v=1775005664","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9783540306764","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}