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» Learning Object Context for Adaptive Learning Design
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CVPR
2011
IEEE
15 years 1 months ago
What You Saw is Not What You Get: Domain Adaptation Using Asymmetric Kernel Transforms
In real-world applications, “what you saw” during training is often not “what you get” during deployment: the distribution and even the type and dimensionality of features...
Brian Kulis, Kate Saenko, Trevor Darrell
ECML
2007
Springer
16 years 16 days ago
Dual Strategy Active Learning
Abstract. Active Learning methods rely on static strategies for sampling unlabeled point(s). These strategies range from uncertainty sampling and density estimation to multi-factor...
Pinar Donmez, Jaime G. Carbonell, Paul N. Bennett
ICML
2000
IEEE
16 years 7 months ago
Learning Subjective Functions with Large Margins
In manyoptimization and decision problems the objective function can be expressed as a linear combinationof competingcriteria, the weights of whichspecify the relative importanceo...
Claude-Nicolas Fiechter, Seth Rogers
AAAI
2010
15 years 7 months ago
Reinforcement Learning via AIXI Approximation
This paper introduces a principled approach for the design of a scalable general reinforcement learning agent. This approach is based on a direct approximation of AIXI, a Bayesian...
Joel Veness, Kee Siong Ng, Marcus Hutter, David Si...
ICASSP
2009
IEEE
16 years 1 months ago
Wildfire detection using LMS based active learning
A computer vision based algorithm for wildfire detection is developed. The main detection algorithm is composed of four sub-algorithms detecting (i) slow moving objects, (ii) gra...
B. Ugur Töreyin, A. Enis Çetin