Efficient Multiclass Implementations of L1-Regularized Maximum Entropy
| dc.creator | Haffner, Patrick | |
| dc.creator | Phillips, Steven | |
| dc.creator | Schapire, Rob | |
| dc.date | 2005-06-29 | |
| dc.date.accessioned | 2026-07-07T03:23:11Z | |
| dc.date.available | 2026-07-07T03:23:11Z | |
| dc.description | This paper discusses the application of L1-regularized maximum entropy modeling or SL1-Max [9] to multiclass categorization problems. A new modification to the SL1-Max fast sequential learning algorithm is proposed to handle conditional distributions. Furthermore, unlike most previous studies, the present research goes beyond a single type of conditional distribution. It describes and compares a variety of modeling assumptions about the class distribution (independent or exclusive) and various types of joint or conditional distributions. It results in a new methodology for combining binary regularized classifiers to achieve multiclass categorization. In this context, Maximum Entropy can be considered as a generic and efficient regularized classification tool that matches or outperforms the state-of-the art represented by AdaBoost and SVMs. | |
| dc.description | 13 pages, describes new conditional maxent algorithm, to be submitted | |
| dc.identifier | https://arxiv.org/abs/cs/0506101 | |
| dc.identifier | http://arxiv.org/abs/cs/0506101 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/32845 | |
| dc.subject | Machine Learning | |
| dc.subject | Computation and Language | |
| dc.title | Efficient Multiclass Implementations of L1-Regularized Maximum Entropy | |
| dc.type | text |