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» Predictive Hebbian Learning
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JFR
2006
140views more  JFR 2006»
15 years 3 months ago
Improving robot navigation through self-supervised online learning
In mobile robotics, there are often features that, while potentially powerful for improving navigation, prove difficult to profit from as they generalize poorly to novel situations...
Boris Sofman, Ellie Lin, J. Andrew Bagnell, John C...
134
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SIGIR
2008
ACM
15 years 3 months ago
Learning from labeled features using generalized expectation criteria
It is difficult to apply machine learning to new domains because often we lack labeled problem instances. In this paper, we provide a solution to this problem that leverages domai...
Gregory Druck, Gideon S. Mann, Andrew McCallum
128
Voted
AAMAS
2005
Springer
15 years 3 months ago
Learning and Exploiting Relative Weaknesses of Opponent Agents
Agents in a competitive interaction can greatly benefit from adapting to a particular adversary, rather than using the same general strategy against all opponents. One method of s...
Shaul Markovitch, Ronit Reger
146
Voted
BMCBI
2004
140views more  BMCBI 2004»
15 years 3 months ago
What can we learn from noncoding regions of similarity between genomes?
Background: In addition to known protein-coding genes, large amounts of apparently non-coding sequence are conserved between the human and mouse genomes. It seems reasonable to as...
Thomas A. Down, Tim J. P. Hubbard
145
Voted
TJS
2010
182views more  TJS 2010»
15 years 2 months ago
A novel unsupervised classification approach for network anomaly detection by k-Means clustering and ID3 decision tree learning
This paper presents a novel host-based combinatorial method based on k-Means clustering and ID3 decision tree learning algorithms for unsupervised classification of anomalous and ...
Yasser Yasami, Saadat Pour Mozaffari