530 search results - page 1 / 106 » Learning Bounds for Domain Adaptation |
NIPS
2007
13 years 5 months ago
2007
Empirical risk minimization offers well-known learning guarantees when training and test data come from the same domain. In the real world, though, we often wish to adapt a classi...
COLT
2008
Springer
13 years 6 months ago
2008
Springer
We propose a model-based learning algorithm, the Adaptive Aggregation Algorithm (AAA), that aims to solve the online, continuous state space reinforcement learning problem in a de...
ICML
2009
IEEE
14 years 5 months ago
2009
IEEE
The purpose of this paper is three-fold. First, we formalize and study a problem of learning probabilistic concepts in the recently proposed KWIK framework. We give details of an ...
ACML
2009
Springer
13 years 9 months ago
2009
Springer
A fundamental assumption for any machine learning task is to have training and test data instances drawn from the same distribution while having a sufficiently large number of tra...
DAGM
2011
Springer
12 years 4 months ago
2011
Springer
The supervised learning paradigm assumes in general that both training and test data are sampled from the same distribution. When this assumption is violated, we are in the setting...