{"product_id":"sparse-learning-under-regularization-framework-von-haiqin-yang-irwin-king-und-michael-r-lyu","title":"Sparse Learning Under Regularization Framework","description":"\u003cp\u003eRegularization is a dominant theme in machine  learning and statistics due to its prominent ability  in providing an intuitive and principled tool for  learning from high-dimensional data.  As large-scale  learning applications become popular, developing  efficient algorithms and parsimonious models become  promising and necessary for these applications.   Aiming at solving large-scale learning problems, this  book tackles the key research problems ranging from  feature selection to learning with mixed unlabeled  data and learning data similarity representation.   More specifically, we focus on the problems in three  areas: online learning, semi-supervised learning, and  multiple kernel learning.  The proposed models can be  applied in various applications, including marketing  analysis, bioinformatics, pattern recognition, etc.\u003c\/p\u003e\u003cdiv class=\"aw-variant-hidden-subtitle-div\" id=\"aw-variant-subtitle-9783844330304\"\u003e\u003ch3\u003eTheory and Applications\u003c\/h3\u003e\u003c\/div\u003e","brand":"Autorenwelt Shop","offers":[{"title":"Softcover - 9783844330304","offer_id":39470009253981,"sku":"9783844330304","price":59.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0940\/0622\/files\/0679e4b6-dadd-494a-bf50-6c2b7ba391a2.jpg?v=1757828314","url":"https:\/\/shop.autorenwelt.de\/products\/sparse-learning-under-regularization-framework-von-haiqin-yang-irwin-king-und-michael-r-lyu","provider":"Autorenwelt Shop","version":"1.0","type":"link"}