.png){kind=icon}
.png){kind=icon}
Return
ECOC-DRF: Discriminative random fields based on error correcting output codes
.png){kind=icon}
OA
AI
DOI：10.1016/j.patcog.2013.12.007.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We present ECOC-DRF, a framework where potential functions for Discriminative Random Fields are formulated as an ensemble of classifiers. We introduce the label trick, a technique to express transitions in the pairwise potential as meta-classes. This allows to independently learn any possible transition between labels without assuming any pre-defined model. The Error Correcting Output Codes matrix is used as ensemble framework for the combination of margin classifiers. We apply ECOC-DRF to a large set of classification problems, covering synthetic, natural and medical images for binary and multi-class cases, outperforming state-of-the art in almost all the experiments. (C) 2013 Elsevier Ltd. All rights reserved.
Keywords:
Discriminative random fields
Error-correcting output codes
Multi-class classification
Graphical models
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W