Name
Abstract | ||
|---|---|---|
This paper is concerned with learning binary classifiers under adversarial label-noise. We introduce the problem of error-correction in learning where the goal is to recover the original clean data from a label-manipulated version of it, given (i) no constraints on the adversary other than an upper-bound on the number of errors, and (ii) some regularity properties for the original data. We present a simple and practical error-correction algorithm called SubSVMs that learns individual SVMs on several small-size (log-size), class-balanced, random subsets of the data and then reclassifies the training points using a majority vote. Our analysis reveals the need for the two main ingredients of SubSVMs, namely class-balanced sampling and subsampled bagging. Experimental results on synthetic as well as benchmark UCI data demonstrate the effectiveness of our approach. In addition to noise-tolerance, log-size subsampled bagging also yields significant run-time benefits over standard SVMs. |
Year | Venue | Field |
|---|---|---|
2013 | CoRR | Pattern recognition,Computer science,Support vector machine,Error detection and correction,Artificial intelligence,Sampling (statistics),Majority rule,Machine learning,Binary number |
DocType | Volume | Citations |
Journal | abs/1301.2012 | 2 |
PageRank | References | Authors |
0.38 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Srivatsan Laxman | 1 | 421 | 21.65 |
| Sushil Mittal | 2 | 89 | 5.45 |
| Ramarathnam Venkatesan | 3 | 1326 | 111.13 |