Name
Abstract | ||
|---|---|---|
We consider the problem of multiclass classification. Our main thesis is that a simple "one-vs-all" scheme is as accurate as any other approach, assuming that the underlying binary classifiers are well-tuned regularized classifiers such as support vector machines. This thesis is interesting in that it disagrees with a large body of recent published work on multiclass classification. We support our position by means of a critical review of the existing literature, a substantial collection of carefully controlled experimental work, and theoretical arguments. |
Year | Venue | Keywords |
|---|---|---|
2004 | Journal of Machine Learning Research | existing literature,critical review,main thesis,support vector machine,substantial collection,large body,regularized classifier,multiclass classification,experimental work,regularization,one-vs-all classification,recent published work |
Field | DocType | Volume |
Structured support vector machine,Pattern recognition,Support vector machine,Artificial intelligence,Mathematics,Machine learning,Multiclass classification,Binary number | Journal | 5, |
ISSN | Citations | PageRank |
1532-4435 | 667 | 31.93 |
References | Authors | |
25 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ryan Rifkin | 1 | 709 | 49.57 |
| Aldebaro Klautau | 2 | 852 | 61.26 |