A new classifier based on information theoretic learning with unlabeled data.
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.
Jeong
Kyu-Hwa
KH
Computational NeuroEngineering Laboratory, Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA. khjeong@cnel.ufl.edu
Xu
Jian-Wu
JW
Erdogmus
Deniz
D
Principe
Jose C
JC
D000465:Algorithms; D001185:Artificial Intelligence; D002965:Classification; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004594:Electrophysiology; D006801:Humans; D007257:Information Theory; D010300:Parkinson Disease / Q000503:physiopathology; D011338:Probability Theory