Name
Title | ||
|---|---|---|
Transductive Learning Machine Based on the Affinity-Rule for Semi-supervised Problems and Its Algorithm |
Abstract | ||
|---|---|---|
One of the central problems in machine learning is how to effectively combine unlabelled and labelled data to infer the labels of unlabelled ones. In this article, transductive learning machines are introduced based on a so-called affinity rule that if two objects are close in input space then their outputs should also be close, to obtain the solution of semi-supervised learning problems. The analytic solution for the problem and its iterated algorithm are obtained. Some simulations about pattern classification are conducted to demonstrate the validity of the proposed method in different situations. Ark incremental learning algorithm adapting to on-line data processing is also derived. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-28647-9_42 | ADVANCES IN NEURAL NETWORKS - ISNN 2004, PT 1 |
Keywords | Field | DocType |
transductive learning,data processing,machine learning,semi supervised learning,analytic solution,iterative algorithm | Transduction (machine learning),Instance-based learning,Semi-supervised learning,Active learning (machine learning),Computer science,Wake-sleep algorithm,Unsupervised learning,Artificial intelligence,Online machine learning,Stability (learning theory),Pattern recognition,Algorithm,Machine learning | Conference |
Volume | ISSN | Citations |
3173 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 7 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weijiang Long | 1 | 6 | 1.20 |
| Wen-xiu Zhang | 2 | 3187 | 116.92 |