377 search results - page 1 / 76 » A Value Theory of Meta-Learning Algorithms |
AAAI
2006
13 years 6 months ago
2006
We use game theory to analyze meta-learning algorithms. The objective of meta-learning is to determine which algorithm to apply on a given task. This is an instance of a more gene...
ICML
2000
IEEE
14 years 6 months ago
2000
IEEE
Landmarking is a novel approach to describing tasks in meta-learning. Previous approaches to meta-learning mostly considered only statistics-inspired measures of the data as a sou...
JMIV
2010
13 years 3 months ago
2010
Motivated by the setting of reproducing kernel Hilbert space (RKHS) and its extensions considered in machine learning, we propose an RKHS framework for image and video colorizatio...
CP
2004
Springer
13 years 10 months ago
2004
Springer
The success of stochastic algorithms is often due to their ability to effectively amplify the performance of search heuristics. This is certainly the case with stochastic sampling ...
JMLR
2010
13 years 2 days ago
2010
We present a general method for explaining individual predictions of classification models. The method is based on fundamental concepts from coalitional game theory and prediction...