: In order to scale to problems with large or continuous state-spaces, reinforcement learning algorithms need to be combined with function approximation techniques. The majority of...
We propose to use AdaBoost to efficiently learn classifiers over very large and possibly distributed data sets that cannot fit into main memory, as well as on-line learning wher...
We present an approach for online learning of discriminative appearance models for robust multi-target tracking in a crowded scene from a single camera. Although much progress has...
— In this paper we propose an on-line incremental vision-based topological map learning for AIBO robots. The topological map is represented through a graph, where the vertices en...
The paper studies machine learning problems where each example is described using a set of Boolean features and where hypotheses are represented by linear threshold elements. One ...