Many kernel learning methods have to assume parametric forms for the target kernel functions, which significantly limits the capability of kernels in fitting diverse patterns. Som...
While discriminative training (e.g., CRF, structural SVM) holds much promise for machine translation, image segmentation, and clustering, the complex inference these applications ...
Email worms continue to be a persistent problem, indicating that current approaches against this class of selfpropagating malicious code yield rather meagre results. Additionally,...
Probabilistic functional integrated networks are powerful tools with which to draw inferences from high-throughput data. However, network analyses are generally not tailored to spe...
We propose a new method for measuring the semantic similarity of genes based on path length between their annotation terms in the Gene Ontology. Our method applies an exponential ...