We present new, general-purpose kernels for protein structure analysis, and describe how to apply them to structural motif discovery and function classification. Experiments show ...
We show that a classifier based on Gaussian mixture models (GMM) can be trained discriminatively to improve accuracy. We describe a training procedure based on the extended Baum-W...
We analyze skewing, an approach that has been empirically observed to enable greedy decision tree learners to learn "difficult" Boolean functions, such as parity, in the...
Bernard Rosell, Lisa Hellerstein, Soumya Ray, Davi...
Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means clustering is a commonly used data clustering for unsupervi...
Many machine learning algorithms can be formulated in the framework of statistical independence such as the Hilbert Schmidt Independence Criterion. In this paper, we extend this c...
Xinhua Zhang, Le Song, Arthur Gretton, Alex J. Smo...