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...
Probabilistic model building methods can render difficult problems feasible by identifying and exploiting dependencies. They build a probabilistic model from the statistical prope...
We present a novel Bayesian approach to the problem of value function estimation in continuous state spaces. We define a probabilistic generative model for the value function by i...
As organization-based multiagent systems are applied to more complex problems, configuring and tuning the systems can become nearly as complex as the original problem a system wa...
Scott J. Harmon, Scott A. DeLoach, Robby, Doina Ca...
Motivated by the real-world application of categorizing system log messages into defined situation categories, this paper describes an interactive text categorization method, PICC...