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» The Dark Side of Object Learning: Learning Objects
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FOCS
1990
IEEE
15 years 7 months ago
Separating Distribution-Free and Mistake-Bound Learning Models over the Boolean Domain
Two of the most commonly used models in computational learning theory are the distribution-free model in which examples are chosen from a fixed but arbitrary distribution, and the ...
Avrim Blum
NIPS
2000
15 years 5 months ago
Learning Winner-take-all Competition Between Groups of Neurons in Lateral Inhibitory Networks
It has long been known that lateral inhibition in neural networks can lead to a winner-take-all competition, so that only a single neuron is active at a steady state. Here we show...
Xiaohui Xie, Richard H. R. Hahnloser, H. Sebastian...
ATAL
2011
Springer
14 years 3 months ago
Learning action models for multi-agent planning
In multi-agent planning environments, action models for each agent must be given as input. However, creating such action models by hand is difficult and time-consuming, because i...
Hankz Hankui Zhuo, Hector Muñoz-Avila, Qian...
CHI
2007
ACM
16 years 4 months ago
Hard lessons: effort-inducing interfaces benefit spatial learning
Interface designers normally strive for a design that minimises the user's effort. However, when the design's objective is to train users to interact with interfaces tha...
Andy Cockburn, Per Ola Kristensson, Jason Alexande...
MLG
2007
Springer
15 years 10 months ago
Learning Graph Matching
As a fundamental problem in pattern recognition, graph matching has found a variety of applications in the field of computer vision. In graph matching, patterns are modeled as gr...
Alex J. Smola