The notions of distance and similarity play a key role in many machine learning approaches,
and artificial intelligence in general, since they can serve as an organizing principle by …

Applying computational statistics or machine learning methods to data is a key component of
many scientific studies, in any field, but alone might not be sufficient to generate robust and …

Reducing the dimensionality of data without losing intrinsic information is an important
preprocessing step in high-dimensional data analysis. Fisher discriminant analysis (FDA) is …

A new kernel function between two labeled graphs is presented. Feature vectors are defined
as the counts of label paths produced by random walks on graphs. The kernel computation …

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 in particular have been successful
in various learning tasks on data represented in a single table. Much'real-world'data …

When only a small number of labeled samples are available, supervised dimensionality
reduction methods tend to perform poorly because of overfitting. In such cases, unlabeled …

Kernel-based learning (eg, Support Vector Machines) has been successfully applied to
many hard problems in Natural Language Processing (NLP). In NLP, although feature …

This paper presents an application of Boosting for classifying labeled graphs, general
structures for modeling a number of real-world data, such as chemical compounds, natural …

This book is a significant contribution to the subject of mining time-changing data streams
and addresses the design of learning algorithms for this purpose. It introduces new …