The increasing wealth of biological data coming from a large variety of platforms and the
continued development of new high-throughput methods for probing biological systems require …

Evaluating the performance of a classification algorithm critically requires a measure of the
degree to which unseen examples have been identified with their correct class labels. In …

Motivation: Computational approaches to protein function prediction infer protein function by
finding proteins with similar sequence, structure, surface clefts, chemical properties, amino …

The fundamental mathematical tools needed to understand machine learning include linear
algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability …

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 …

In many applications it is desirable to learn from several kernels. "Multiple kernel learning" (MKL)
allows the practitioner to optimize over linear combinations of kernels. By enforcing …

In this paper we show that many kernel methods can be adapted to deal with indefinite kernels,
that is, kernels which are not positive semidefinite. They do not satisfy Mercer's condition …

Open source tools have recently reached a level of maturity which makes them suitable for
building large-scale real-world systems. At the same time, the field of machine learning has …

Many supervised learning problems involve learning from samples whose labels are corrupted
in some way. For example, each sample may have some constant probability of being …

Wheel defects on railway wagons have been identified as an important source of damage to
the railway infrastructure and rolling stock. They also cause noise and vibration emissions …