Exponential models of distributions are widely used in machine learning for classification and modelling. It is well known that they can be interpreted as maximum entropy models u...
In this work a cooperative, bid-based, model for problem decomposition is proposed with application to discrete action domains such as classification. This represents a significan...
This paper considers the problems of feature variation and concept uncertainty in typical learning-based video semantic classification schemes. We proposed a new online semantic c...
Argumentation is a promising approach used by autonomous agents for reasoning about inconsistent/incomplete/uncertain knowledge, based on the construction and the comparison of ar...
We propose abc-boost (adaptive base class boost) for multi-class classification and present abc-mart, an implementation of abcboost, based on the multinomial logit model. The key ...