453 search results - page 4 / 91 » Information Theory, Inference, and Learning Algorithms |
AIIA
2003
Springer
14 years 1 months ago
2003
Springer
A bayesian network is an appropriate tool for working with uncertainty and probability, that are typical of real-life applications. In literature we find different approaches for b...
ALT
2007
Springer
14 years 2 months ago
2007
Springer
In transfer learning the aim is to solve new learning tasks using fewer examples by using information gained from solving related tasks. Existing transfer learning methods have be...
CORR
2012
Springer
12 years 3 months ago
2012
Springer
Determinantal point processes (DPPs), which arise in random matrix theory and quantum physics, are natural models for subset selection problems where diversity is preferred. Among...
AAAI
2006
13 years 9 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
1999
IEEE
14 years 8 months ago
1999
IEEE
Most machine learning algorithms share the following drawback: they only output bare predictions but not the con dence in those predictions. In the 1960s algorithmic information t...