Evaluation and Selection of Biases in Machine Learning
Machine Learning - Special issue on bias evaluation and selection
Tracking Context Changes through Meta-Learning
Machine Learning - Special issue on multistrategy learning
Reinforcement learning with self-modifying policies
Learning to learn
Reinforcement Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A perspective view and survey of meta-learning
Artificial Intelligence Review
Competition Among Sellers in Online Exchanges
Information Systems Research
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
| Hi-index | 0.00 |
Many learning and heuristic search algorithms require tuning of parameters to achieve optimum performance. In stationary and deterministic problem domains this is usually achieved through off-line sensitivity analysis. However, this method breaks down in non-stationary and non-deterministic environments, where the optimal set of values for the parameters keep changing over time. What is needed in such scenarios is a meta-learning (ML) mechanism that can learn the optimal set of parameters on-line while the learning algorithm is trying to learn its target concept. In this paper, we present a simple meta-learning algorithm to learn the temperature parameter of the Softmax reinforcement-learning (RL) algorithm. We present results to show the efficacy of this meta-learning algorithm in two domains.