Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

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 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 …

Fisher score is one of the most widely used supervised feature selection methods. However,
it selects each feature independently according to their scores under the Fisher criterion …

摘要多核学习方法是当前核机器学习领域的一个新的热点. 核方法是解决非线性模式分析问题的
一种有效方法, 但在一些复杂情形下, 由单个核函数构成的核机器并不能满足诸如数据异构或不 …

Regions of interest (ROIs) based classification has been widely investigated for analysis of
brain magnetic resonance imaging (MRI) images to assist the diagnosis of Alzheimer's …

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) …

Feature selection has attracted a huge amount of interest in both research and application
communities of data mining. We consider the problem of semi-supervised feature selection …

We consider the problem of how to improve the efficiency of Multiple Kernel Learning (MKL).
In literature, MKL is often solved by an alternating approach:(1) the minimization of the …