In recent years, an enormous amount of research has been carried out on support vector
machines (SVMs) and their application in several fields of science. SVMs are one of the …

In many real-world deployments of machine learning systems, data arrive piecemeal. These
learning scenarios may be passive, where data arrive incrementally due to structural …

Multiple kernel learning aims at simultaneously learning a kernel and the associated
predictor in supervised learning settings. For the support vector machine, an efficient and …

The support vector machine (SVM) is a widely used tool for classification. Many efficient
implementations exist for fitting a two-class SVM model. The user has to supply values for …

Choosing optimal hyperparameter values for support vector machines is an important step in
SVM design. This is usually done by minimizing either an estimate of generalization error or …

Practical experience has shown that in order to obtain the best possible performance, prior
knowledge about invariances of a classification problem at hand ought to be incorporated …

This paper develops a fast method for solving linear SVMs with L2 loss function that is suited
for large scale data mining tasks such as text classification. This is done by modifying the …

An efficient and general multiple kernel learning (MKL) algorithm has been recently
proposed by Sonnenburg et al.(2006). This approach has opened new perspectives since it …

Spam is a key problem in electronic communication, including large-scale email systems
and the growing number of blogs. Content-based filtering is one reliable method of …

Learning from data is one of the basic ways humans perceive the world and acquire the
knowledge. Support vector machine (SVM for short) has emerged as a good classification …