In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.
Populaire auteurs
Cram101 Textbook Reviews (948) J.S. Bach (447) Wolfgang Amadeus Mozart (305) Collectif (268) Schrijf als eerste een recensie over dit item (259) Doug Gelbert (238) Princess of Patterns (211) Charles Dickens (209) R.B. Grimm (197) Carolyn Keene (187) Jules Verne (183) Philipp Winterberg (180) William Shakespeare (174) Youscribe (172) Lucas Nicolato (169) Edgar Allan Poe (166) Herman Melville (166) Anonymous (165) Gilad Soffer (164) Robert Louis Stevenson (159)Populaire gewichtsboeken
418 KB 425 KB 435 KB 459 KB 445 KB 439 KB 386 KB 413 KB 493 KB 432 KB 455 KB 471 KB 421 KB 451 KB 485 KB 472 KB 416 KB 369 KB 419 KB 427 KB