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

Kernel methods in general, and support vector machines (SVMs) in particular, are
increasingly used to solve various problems in computational biology. They offer versatile …

Sure to be influential, Watanabe's book lays the foundations for the use of algebraic
geometry in statistical learning theory. Many models/machines are singular: mixture models …

A new hidden Markov model (HMM) based feature generation scheme is proposed for face
recognition (FR) in this paper. In this scheme, HMM method is used to model classes of face …

The work proposed in this paper is motivated by the need to develop powerful models and
approaches to classify and learn proportional data. Indeed, an abundance of interesting …

Quantum kernel methods exploit quantum computers to calculate quantum kernels (QKs) for
the use of kernel-based learning models. Despite a potential quantum advantage of the …

Making statements about the performance of trained models on tasks involving new data is
one of the primary goals of machine learning, ie, to understand the generalization power of a …

This work introduces a novel data mining scheme, spatial pyramid mining, to discover
association rules at multiple resolutions in order to identify frequent spatial configurations of …

This manuscript provides a comprehensive exploration of Probabilistic latent semantic
analysis (PLSA), highlighting its strengths, drawbacks, and challenges. The PLSA, originally …

The basic idea behind the Fisher kernel method is to train a (generative) hidden Markov
model (HMM) on data to derive a Fisher kernel for a (discriminative) support vector machine …