| Paper: | MLSP-L2.3 | ||
| Session: | Kernel Machines | ||
| Time: | Wednesday, May 17, 17:10 - 17:30 | ||
| Presentation: | Lecture | ||
| Topic: | Machine Learning for Signal Processing: Graphical and kernel models | ||
| Title: | Semi-Supervised Kernel Methods for Regression Estimation | ||
| Authors: | Alexei Pozdnoukhov, Samy Bengio, IDIAP Research Institute, Switzerland | ||
| Abstract: | The paper presents a semi-supervised kernel method for regression estimation in the presence of unlabelled patterns. The method exploits a recently proposed data-dependent kernel which is constucted 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 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. | ||
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