Abstract:
Traditional data-driven classifier learning approaches become limited when the training data is inadequate either in quantity or quality. To address this issue, in this p...Show MoreMetadata
Abstract:
Traditional data-driven classifier learning approaches become limited when the training data is inadequate either in quantity or quality. To address this issue, in this paper we propose to combine hidden information and data to enhance classifier learning. Hidden information represents information that is only available during training but not available during testing. It often exists in many applications yet has not been thoroughly exploited, and existing methods to utilize hidden information are still limited. To this end, we propose two general approaches to exploit different types of hidden information to improve different classifiers. We also extend the proposed methods to deal with incomplete hidden information. Experimental results on different applications demonstrate the effectiveness of the proposed methods for exploiting hidden information and their superior performance to existing methods.
Date of Conference: 07-12 June 2015
Date Added to IEEE Xplore: 15 October 2015
ISBN Information: