New Learning Approach by Co-training for Complex Data Classification
Salma Kammoun Jarraya, Rania Rebai Boukhriss, Emna Jammouci Fendri, Mohamed Hammami HanÃƒÂªne Ben-Abdallah
Progress in Machines and Systems, Vol. 2, No. 1, 2013.
DOI: 
Link: http://www.dline.info/pms/fulltext/v2n1/4.pdf
Abstract:
The complexity, abundance and diversity of data (text, images, sound and video) are at the origin of new software solutions for their exploitation. These solutions make use of learning techniques. Among of these techniques, we distinguish learning through co-training, which turned out to be a very exploited by researchers thanks to its low sensitivity to noise, robustness and rapidity. However, the classical variant of co-training showed deficiencies in the classification of complex data particularly in terms of adaptability. In this paper, we present a new approach of semi-supervised learning by Co-training technique to generate a generic classifier from a representative database (MGLP) which is automatically labeled online. Robustness and rapidity, of our approach, to classify complex data were demonstrated through a comparison between the classical variant and the new one in terms of rate and time.