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 …

Combining information from various data sources has become an important research topic
in machine learning with many scientific applications. Most previous studies employ kernels …

This paper presents a novel optimized kernel k-means algorithm (OKKC) to combine
multiple data sources for clustering analysis. The algorithm uses an alternating minimization …

The performance of the most clustering algorithms highly relies on the representation of data
in the input space or the Hilbert space of kernel methods. This paper is to obtain an …

For most problems in science and engineering we can obtain data sets that describe the
observed system from various perspectives and record the behavior of its individual …

Convex optimization problems arising in applications, possibly as approximations of
intractable problems, are often structured and large scale. When the data are noisy, it is of …

Extreme learning machine (ELM) has been an important research topic over the last decade
due to its high efficiency, easy-implementation, unification of classification and regression …

In this paper we describe our TRECVID 2005 experiments. The UvA-MediaMill team
participated in four tasks. For the detection of camera work (runid: A CAM) we investigate the …

Many problems in statistics and machine learning (eg, probabilistic graphical model, feature
extraction, clustering and classification, etc) can be (re) formulated as linearly constrained …

The principle of parsimony also known as “Ockham's razor” has inspired many theories of
model selection. Yet such theories, all making arguments in favor of parsimony, are based …