Concept drifting is an important and challenging research issue in the field of machine learning. This paper mainly addresses the issue of semantic concept drifting in time series...
This paper presents the design and implementation of an adaptive open-set speaker identification system with genetic learning classifier systems. One of the challenging problems i...
WonKyung Park, Jae C. Oh, Misty K. Blowers, Matt B...
In this paper we introduce a method for computing fitness in evolutionary learning systems based on NVIDIA’s massive parallel technology using the CUDA library. Both the match ...
We consider the problem of learning density mixture models for classification. Traditional learning of mixtures for density estimation focuses on models that correctly represent t...
This paper introduces a new variety of learning classifier system (LCS), called MILCS, which utilizes mutual information as fitness feedback. Unlike most LCSs, MILCS is specifical...