Title :
A Novel Transductive Learning Algorithm Based on Multi-Agent-System
Author_Institution :
Oujiang Coll., Wenzhou Univ. Wenzhou, Wenzhou, China
Abstract :
We consider the problem of multiclass classification where both a few labeled data and lots of unlabeled data are given, for which a new approach called multi-agent-system-based multi-class transductive learning (MMT) is presented. In MMT, we transform the data classification into a self-organizing Markov stochastic process that finally converges to a stationary probability distribution, in which an optimal label distribution is provided. Based on the proposed approach, an algorithm called multi-agent-system-based multi-class transductive algorithm (MMTA) was designed and its converging capabilities were discussed. The simulations have shown the effectiveness and practicability of MMTA.
Keywords :
Markov processes; learning (artificial intelligence); multi-agent systems; pattern classification; statistical distributions; data classification; multiagent-system-based multiclass transductive algorithm; multiclass classification; multiclass transductive learning; optimal label distribution; selforganizing Markov stochastic process; stationary probability distribution; unlabeled data; Algorithm design and analysis; Educational institutions; Electronic mail; Information technology; Markov processes; Probability distribution; Semisupervised learning; Stochastic processes; Support vector machine classification; Support vector machines; multi-agent-system; multi-class; semi-supervised learning; transductive learning;
Conference_Titel :
Intelligent Information Technology Application, 2009. IITA 2009. Third International Symposium on
Conference_Location :
Nanchang
Print_ISBN :
978-0-7695-3859-4
DOI :
10.1109/IITA.2009.262
