Transductive Minimax Probability Machine

Autor:	Kilian Q. Weinberger, Zhixiang Eddie Xu, Gao Huang, Shiji Song
Rok vydání:	2014
Předmět:	Transduction (machine learning) Optimization problem Computer science business.industry Semi-supervised learning Machine learning computer.software_genre Minimax Upper and lower bounds Generalization error Test set Artificial intelligence business computer Classifier (UML) Algorithm
Zdroj:	Machine Learning and Knowledge Discovery in Databases ISBN: 9783662448472 ECML/PKDD (1)
DOI:	10.1007/978-3-662-44848-9_37
Popis:	The Minimax Probability Machine (MPM) is an elegant machine learning algorithm for inductive learning. It learns a classifier that minimizes an upper bound on its own generalization error. In this paper, we extend its celebrated inductive formulation to an equally elegant transductive learning algorithm. In the transductive setting, the label assignment of a test set is already optimized during training. This optimization problem is an intractable mixed-integer programming. Thus, we provide an efficient label-switching approach to solve it approximately. The resulting method scales naturally to large data sets and is very efficient to run. In comparison with nine competitive algorithms on eleven data sets, we show that the proposed Transductive MPM (TMPM) almost outperforms all the other algorithms in both accuracy and speed.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::c310ec9594087729c69b902f87b525ac https://doi.org/10.1007/978-3-662-44848-9_37 Zobrazit plný text záznamu