— This paper addresses model reduction for a Markov chain on a large state space. A simulation-based framework is introduced to perform state aggregation of the Markov chain base...
This paper proposes a multiple instance learning (MIL) algorithm for Gaussian processes (GP). The GP-MIL model inherits two crucial benefits from GP: (i) a principle manner of lea...
Abstract--We present an algorithm that coevolves fitness predictors, optimized for the solution population, which reduce fitness evaluation cost and frequency, while maintaining ev...
Finding an accurate sparse approximation of a spectral vector described by a linear model, when there is available a library of possible constituent signals (called endmembers or ...
The present paper mainly studies the expected teaching time of memoryless randomized learners without feedback. First, a characterization of optimal randomized learners is provided...