A new classifier based on information theoretic learning with unlabeled data.

Kyu-Hwa Jeong; Jian-Wu Xu; Deniz Erdogmus; Jose C Principe

doi:10.1016/j.neunet.2005.06.018

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A new classifier based on information theoretic learning with unlabeled data.

Kyu-Hwa Jeong·Jian-Wu Xu·Deniz Erdogmus·Jose C Principe

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Abstract

Supervised learning is conventionally performed with pairwise input-output labeled data. After the training procedure, the adaptive system's weights are fixed while the testing procedure with unlabeled data is performed. Recently, in an attempt to improve classification performance unlabeled data has been exploited in the machine learning community. In this paper, we present an information theoretic learning (ITL) approach based on density divergence minimization to obtain an extended training algorithm using unlabeled data during the testing. The method uses a boosting-like algorithm with an ITL based cost function. Preliminary simulations suggest that the method has the potential to improve the performance of classifiers in the application phase.

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Algorithms Artificial Intelligence Classification Computer Simulation Data Interpretation, Statistical Electrophysiology Humans Information Theory Parkinson Disease Probability Theory Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

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Kyu-Hwa Jeong, Jian-Wu Xu, Deniz Erdogmus, Jose C Principe. A new classifier based on information theoretic learning with unlabeled data.. https://doi.org/10.1016/j.neunet.2005.06.018

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