{"product_id":"combinatorial-machine-learning-a-rough-set-approach-von-mikhail-moshkov-beata-zielosko","title":"Combinatorial Machine Learning","description":"\n                                \n                \u003cp\u003eDecision trees and decision rule systems are widely used in different applications\u003c\/p\u003e\n                                \n                \u003cp\u003eas algorithms for problem solving, as predictors, and as a way for\u003c\/p\u003e\n                                \n                \u003cp\u003eknowledge representation. Reducts play key role in the problem of attribute\u003c\/p\u003e\n                                \n                \u003cp\u003e(feature) selection. The aims of this book are (i) the consideration of the sets\u003c\/p\u003e\n                                \n                \u003cp\u003eof decision trees, rules and reducts; (ii) study of relationships among these\u003c\/p\u003e\n                                \n                \u003cp\u003eobjects; (iii) design of algorithms for construction of trees, rules and reducts;\u003c\/p\u003e\n                                \n                \u003cp\u003eand (iv) obtaining bounds on their complexity. Applications for supervised\u003c\/p\u003e\n                                \n                \u003cp\u003emachine learning, discrete optimization, analysis of acyclic programs, fault\u003c\/p\u003e\n                                \n                \u003cp\u003ediagnosis, and pattern recognition are considered also. This is a mixture of\u003c\/p\u003e\n                                \n                \u003cp\u003eresearch monograph and lecture notes. It contains many unpublished results.\u003c\/p\u003e\n                                \n                \u003cp\u003eHowever, proofs are carefully selected to be understandable for students.\u003c\/p\u003e\n                                \n                \u003cp\u003eThe results considered in this book can be useful for researchers in machine\u003c\/p\u003e\n                                \n                \u003cp\u003elearning, data mining and knowledge discovery, especially for those who are\u003c\/p\u003e\n                                \n                \u003cp\u003eworking in rough set theory, test theory and logical analysis of data. The book\u003c\/p\u003e\n                                \n                \u003cp\u003ecan be used in the creation of courses for graduate students.\u003c\/p\u003e\n                            \n            \u003cdiv class=\"aw-variant-hidden-subtitle-div\" id=\"aw-variant-subtitle-9783642269011\"\u003e\u003ch3\u003eA Rough Set Approach\u003c\/h3\u003e\u003c\/div\u003e","brand":"Libri","offers":[{"title":"Softcover - 9783642269011","offer_id":39431132217437,"sku":"9783642269011","price":106.99,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0940\/0622\/files\/b9e344cc-29c9-47aa-9787-d17c1f5f384e.jpg?v=1774678077","url":"https:\/\/shop.autorenwelt.de\/products\/combinatorial-machine-learning-a-rough-set-approach-von-mikhail-moshkov-beata-zielosko","provider":"Autorenwelt Shop","version":"1.0","type":"link"}