{"product_id":"combinatorial-machine-learning-a-rough-set-approach-hardcover","title":"Combinatorial Machine Learning: A Rough Set Approach - Hardcover","description":"\u003cp\u003eby \u003cb\u003eMikhail Moshkov\u003c\/b\u003e (Author), \u003cb\u003eBeata Zielosko\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003eThis book explores decision trees and decision rule systems, rules and reducts, examines relationships among these objects and reviews the design of algorithms for construction of trees, rules and reducts. Includes carefully selected illustrative proofs.\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eDecision trees and decision rule systems are widely used in different applications\u003c\/p\u003e\u003cp\u003eas algorithms for problem solving, as predictors, and as a way for\u003c\/p\u003e\u003cp\u003eknowledge representation. Reducts play key role in the problem of attribute\u003c\/p\u003e\u003cp\u003e(feature) selection. The aims of this book are (i) the consideration of the sets\u003c\/p\u003e\u003cp\u003eof decision trees, rules and reducts; (ii) study of relationships among these\u003c\/p\u003e\u003cp\u003eobjects; (iii) design of algorithms for construction of trees, rules and reducts;\u003c\/p\u003e\u003cp\u003eand (iv) obtaining bounds on their complexity. Applications for supervised\u003c\/p\u003e\u003cp\u003emachine learning, discrete optimization, analysis of acyclic programs, fault\u003c\/p\u003e\u003cp\u003ediagnosis, and pattern recognition are considered also. This is a mixture of\u003c\/p\u003e\u003cp\u003eresearch monograph and lecture notes. It contains many unpublished results.\u003c\/p\u003e\u003cp\u003eHowever, proofs are carefully selected to be understandable for students.\u003c\/p\u003e\u003cp\u003eThe results considered in this book can be useful for researchers in machine\u003c\/p\u003e\u003cp\u003elearning, data mining and knowledge discovery, especially for those who are\u003c\/p\u003e\u003cp\u003eworking in rough set theory, test theory and logical analysis of data. The book\u003c\/p\u003e\u003cp\u003ecan be used in the creation of courses for graduate students.\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 182\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.5 x 9.21 x 6.14 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e June 29, 2011\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":42711327014975,"sku":"9783642209949","price":213.82,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/1d15a026da23d0f89b4dd3bfb04cfead.webp?v=1765058110","url":"https:\/\/dhlswag.com\/products\/combinatorial-machine-learning-a-rough-set-approach-hardcover","provider":"BBB","version":"1.0","type":"link"}