In many design tasks it is difficult to explicitly define an objective function. This paper uses machine learning to derive an objective in a feature space based on selected examp...
Given a set of jobs, each consisting of a number of weighted intervals on the real line, and a number m of machines, we study the problem of selecting a maximum weight subset of th...
This paper studies the dynamic Web service selection problem in a failure-prone environment, which aims to determine a subset of Web services to be invoked at runtime so as to succ...
We propose a kernelized maximal-figure-of-merit (MFoM) learning approach to efficiently training a nonlinear model using subspace distance minimization. In particular, a fixed,...
Constrained pattern mining extracts patterns based on their individual merit. Usually this results in far more patterns than a human expert or a machine learning technique could m...