With the rapid development of spectral imaging techniques, classification of hyperspectral
images (HSIs) has attracted great attention in various applications such as land survey and …

Multiple kernel learning (MKL) is a principled approach for selecting and combining kernels
for a given recognition task. A number of studies have shown that MKL is a useful tool for …

In recent years, a great many methods of learning from multi-view data by considering the
diversity of different views have been proposed. These views may be obtained from multiple …

In order to identify flood-prone areas with limited flood inventories, a semi-supervised
machine learning model—the weakly labeled support vector machine (WELLSVM)—is used …

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 …

It is practical to assume that an individual view is unlikely to be sufficient for effective multi-
view learning. Therefore, integration of multi-view information is both valuable and …

Fusing the advantages of multiple acoustic features is important for the robustness of voice
activity detection (VAD). Recently, the machine-learning-based VADs have shown a …

Learning linear combinations of multiple kernels is an appealing strategy when the right
choice of features is unknown. Previous approaches to multiple kernel learning (MKL) …

Exploiting multiple representations, or views, for the same set of instances within a clustering
framework is a popular practice for boosting clustering accuracy. However, some of the …

Abstract The goal of Multiple Kernel Learning (MKL) is to combine kernels derived from
multiple sources in a data-driven way with the aim to enhance the accuracy of a target kernel …