Approximating non-linear kernels using feature maps has gained a lot of interest in recent years due to applications in reducing training and testing times of SVM classifiers and...
Random projection is a simple technique that has had a number of applications in algorithm design. In the context of machine learning, it can provide insight into questions such as...
This paper provides a probabilistic derivation of an identity connecting the square loss of ridge regression in on-line mode with the loss of a retrospectively best regressor. Some...
Latent Variable Models (LVM), like the Shared-GPLVM
and the Spectral Latent Variable Model, help mitigate over-
fitting when learning discriminative methods from small or
modera...
In this study, we propose a new machine learning model namely, Adaptive Locality-Effective Kernel Machine (Adaptive-LEKM) for protein phosphorylation site prediction. Adaptive-LEK...
Paul D. Yoo, Yung Shwen Ho, Bing Bing Zhou, Albert...