In recent years, several methods have been proposed to combine multiple kernels instead of
using a single one. These different kernels may correspond to using different notions of …

Recently, instead of selecting a single kernel, multiple kernel learning (MKL) has been
proposed which uses a convex combination of kernels, where the weight of each kernel is …

Instead of selecting a single kernel, multiple kernel learning (MKL) uses a weighted sum of
kernels where the weight of each kernel is optimized during training. Such methods assign …

The problem of combining different sources of information arises in several situations, for
instance, the classification of data with asymmetric similarity matrices or the construction of …

Combining classifiers is to join the strengths of different classifiers to improve the
classification performance. Using rules to combine the outputs of different classifiers is the …

Base kernel selection, the task of selecting multiple good kernels, is a key issue in multiple
kernel learning (MKL) algorithms. This paper introduces a new framework for base strong …

For the last decades, anomaly detection has been one of the most common problems in data
mining and computer science projects. The scientific community has made a great effort to …

Kernel theory is a demonstrated tool that has made its way into nearly all areas of machine
learning. However, a serious limitation of kernel methods is knowing which kernel is needed …

This paper describes the combination of support vector machine (SVM) classifiers using
Genetic Programming (GP) for gender classification problem. In our scheme, individual SVM …

Abstract Multiple Kernel Learning (MKL) literature has mostly focused on learning weights
for base kernel combiners. Recent works using instance dependent weights have resulted in …