Time series classification is an increasing research topic due to the vast amount of time
series data that is being created over a wide variety of fields. The particularity of the data …

This paper reviews and extends the field of similarity-based classification, presenting new
analyses, algorithms, data sets, and a comprehensive set of experimental results for a rich …

Kernel methods are a cornerstone of classical machine learning. The idea of using quantum
computers to compute kernels has recently attracted attention. Quantum embedding kernels …

A comprehensive introduction to Support Vector Machines and related kernel methods. In
the 1990s, a new type of learning algorithm was developed, based on results from statistical …

We present an application of kernel methods to extracting relations from unstructured natural
language sources. We introduce kernels defined over shallow parse representations of text …

Graph kernels have attracted a lot of attention during the last decade, and have evolved into
a rapidly develo** branch of learning on structured data. During the past 20 years, the …

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

Usually, objects to be classified are represented by features. In this paper, we discuss an
alternative object representation based on dissimilarity values. If such distances separate …

A new relational clustering method is introduced, based on the Dempster-Shafer theory of
belief functions (or evidence theory). Given a matrix of dissimilarities between n objects, this …

Kernel methods are becoming increasingly popular for various kinds of machine learning
tasks, the most famous being the support vector machine (SVM) for classification. The SVM …