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AUSAI
2005
Springer
13 years 11 months ago
Global Versus Local Constructive Function Approximation for On-Line Reinforcement Learning
: 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...
Peter Vamplew, Robert Ollington
KDD
1999
ACM
199views Data Mining» more  KDD 1999»
13 years 9 months ago
The Application of AdaBoost for Distributed, Scalable and On-Line Learning
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...
Wei Fan, Salvatore J. Stolfo, Junxin Zhang
CVPR
2010
IEEE
13 years 11 months ago
Multi-Target Tracking by On-Line Learned Discriminative Appearance Models
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...
Cheng-Hao Kuo, Chang Huang, Ram Nevatia
ICRA
2007
IEEE
98views Robotics» more  ICRA 2007»
13 years 11 months ago
Incremental On-Line Topological Map Learning for A Visual Homing Application
— 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...
Elvina Motard, Bogdan Raducanu, Viviane Cadenat, J...
NIPS
2001
13 years 6 months ago
Efficiency versus Convergence of Boolean Kernels for On-Line Learning Algorithms
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 ...
Roni Khardon, Dan Roth, Rocco A. Servedio