A detailed overview of current research in kernel methods and their application to
computational biology. Modern machine learning techniques are proving to be extremely …

This paper addresses the problem of choosing a kernel suitable for estimation with a support
vector machine, hence further automating machine learning. This goal is achieved by …

The relationship between two sets of variables defined for the same individuals can be
evaluated by the RV coefficient. However, it is impossible to assess by the RV value alone …

We address the problem of learning a symmetric positive definite matrix. The central issue is
to design parameter updates that preserve positive definiteness. Our updates are motivated …

In recent years, diffusion tensor imaging (DTI) has become a popular in vivo diagnostic
imaging technique in Radiological sciences. In order for this imaging technique to be more …

Motivation: Inference and reconstruction of biological networks from heterogeneous data is
currently an active research subject with several important applications in systems biology …

In this review chapter, we focus on the problem of reconstructing the structure of largescale
biological networks. By biological networks, we mean graphs whose vertices are all or a …

We address the incomplete-data problem in which feature vectors to be classified are
missing data (features). A (supervised) logistic regression algorithm for the classification of …

While distance function learning for supervised learning tasks has a long history, extending
it to learning tasks with weaker supervisory information has only been studied recently. In …

In this paper, we introduce a novel approach, called Input Output Kernel Regression (IOKR),
for learning map**s between structured inputs and structured outputs. The approach …