Title :
Semi-Supervised Kernel Methods for Regression Estimation
Author :
Pozdnoukhov, Alexei ; Bengio, Samy
Author_Institution :
Inst. of IDIAP Res., Ecole Polytech. Fed. de Lausanne
Abstract :
The paper presents a semi-supervised kernel method for regression estimation in the presence of unlabeled patterns. The method exploits a recently proposed data-dependent kernel which is constructed in order to represent the inner geometry of the data. This kernel is implemented into kernel regression methods (SVR, KRR). Experimental results aim to highlight the properties of the method and its advantages as compared to fully supervised approaches. The influence of the parameters on the model properties was evaluated experimentally. One artificial and two real-world datasets were used to demonstrate the performance of the proposed algorithm
Keywords :
geometry; learning (artificial intelligence); regression analysis; data-dependent kernel; geometry; regression estimation; semi-supervised kernel methods; unlabeled patterns; Geometry; Kernel; Machine learning; Machine learning algorithms; Multidimensional signal processing; Semisupervised learning; Signal processing algorithms; Support vector machine classification; Support vector machines; Unsupervised learning;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on
Conference_Location :
Toulouse
Print_ISBN :
1-4244-0469-X
DOI :
10.1109/ICASSP.2006.1661341
