We propose an unconventional but highly effective approach
to robust fitting of multiple structures by using statistical
learning concepts. We design a novel Mercer kernel
for t...
Spectral analysis approaches have been actively studied in machine learning and data mining areas, due to their generality, efficiency, and rich theoretical foundations. As a natur...
Dijun Luo, Heng Huang, Chris H. Q. Ding, Feiping N...
In this paper we formulate the problem of grouping the states of a discrete Markov chain of arbitrary order simultaneously with deconvolving its transition probabilities. As the na...
Several published reports show that instancebased learning algorithms yield high classification accuracies and have low storage requirements during supervised learning application...
Ontology learning is an important task in Artificial Intelligence, Semantic Web and Text Mining. This paper presents a novel framework for, and solutions to, three practical probl...