The use of Mercer kernel methods in statistical learning theory provides for strong learning capabilities, as seen in kernel principal component analysis and support vector machin...
In recent years, a fundamental problem structure has emerged as very useful in a variety of machine learning applications: Submodularity is an intuitive diminishing returns proper...
Level set methods have been widely used in image processing and computer vision. In conventional level set formulations, the level set function typically develops irregularities du...
Chunming Li, Chenyang Xu, Changfeng Gui, Martin D....
In this paper, we develop a stochastic approximation method to solve a monotone estimation problem and use this method to enhance the empirical performance of the Q-learning algor...
In this second part of our state-of-the-art overview on aggregation theory, based again on our recent monograph on aggregation functions, we focus on several construction methods ...
Michel Grabisch, Jean-Luc Marichal, Radko Mesiar, ...