LEADER 02816nam 22004813 450 001 9910583058303321 005 20250204111017.0 010 $a9780081006597 010 $a0081006594 035 $a(CKB)4100000000918851 035 $a(MiAaPQ)EBC5754505 035 $a(MiFhGG)9780081006702 035 $a(BIP)57036599 035 $a(EXLCZ)994100000000918851 100 $a20210428d2017 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning $eA Constraint-Based Approach 205 $a1ª Ed. 210 1$aSaint Louis :$cElsevier Science & Technology,$d2017. 210 4$d©2018. 215 $a1 Recurso online 330 $aMachine Learning: A Constraint-Based Approach provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphasis on current topics of interest that includes neural networks and kernel machines. The book presents the information in a truly unified manner that is based on the notion of learning from environmental constraints. While regarding symbolic knowledge bases as a collection of constraints, the book draws a path towards a deep integration with machine learning that relies on the idea of adopting multivalued logic formalisms, like in fuzzy systems. A special attention is reserved to deep learning, which nicely fits the constrained- based approach followed in this book. This book presents a simpler unified notion of regularization, which is strictly connected with the parsimony principle, and includes many solved exercises that are classified according to the Donald Knuth ranking of difficulty, which essentially consists of a mix of warm-up exercises that lead to deeper research problems. A software simulator is also included. Presents fundamental machine learning concepts, such as neural networks and kernel machines in a unified manner Provides in-depth coverage of unsupervised and semi-supervised learning Includes a software simulator for kernel machines and learning from constraints that also includes exercises to facilitate learning Contains 250 solved examples and exercises chosen particularly for their progression of difficulty from simple to complex 606 $aMachine learning 606 $aAlgorithms 606 $aAlgorithms$2fast$3(OCoLC)fst00805020 606 $aMachine learning$2fast$3(OCoLC)fst01004795 615 0$aMachine learning. 615 0$aAlgorithms. 615 7$aAlgorithms. 615 7$aMachine learning. 676 $a006.3/1 700 $aGori$b Marco$062040 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910583058303321 996 $aMachine Learning$92163398 997 $aUNINA