Transfer Learning Using the Minimum Description Length Principle with a Decision Tree Application
نویسنده
چکیده
Transfer learning is about how learning from one domain or a collection of domains can be applied to another. It is learning from similarities and parallels, from experience. This paper is about a distribution free, data driven, extendable framework for transfer learning, based on the minimum description length principle. We define transfer learning in terms of a specific framework, where we have a collection of hypothesis from other application domains available, but not the data, a learning algorithm consistent over domains and a new, previously unseen learning task.
منابع مشابه
Context Maximizing : Finding MDL Decision Trees
We present an application of the context weighting algorithm. Our objective is to classify objects with decision trees. The best tree will be searched for with the Minimum Description Length Principle. In order to find these trees, we modified the context weighting algorithm.
متن کاملAttribute Value Selection Considering the Minimum Description Length Approach and Feature Granularity
In this paper we introduce a new approach to automatic attribute and granularity selection for building optimum regression trees. The method is based on the minimum description length principle (MDL) and aspects of granular computing. The approach is verified by giving an example using a data set which is extracted and preprocessed from an operational information system of the Components Toolsh...
متن کاملPaper Learning Bayesian Belief Networks Based on the Minimum Description Length Principle: Basic Properties
SUMMARY This paper addresses the problem of learning Bayesian belief networks (BBN) based on the minimum description length (MDL) principle. First, we give a formula of description length based on which the MDL-based procedure learns a BBN. Secondly, we point out that the diierence between the MDL-based and Cooper and Herskovits procedures is essentially in the priors rather than in the approac...
متن کاملGenetic Programming Using a Minimum Description Length Principle
This paper introduces a Minimum Description Length (MDL) principle to de ne tness functions in Genetic Programming (GP). In traditional (Koza-style) GP, the size of trees was usually controlled by user-de ned parameters, such as the maximum number of nodes and maximum tree depth. Large tree sizes meant that the time necessary to measure their tnesses often dominated total processing time. To ov...
متن کاملLearning Bayesian Belief Networks Based on the Minimum Description Length Principle : Basic Properties ∗
SUMMARY This paper addresses the problem of learning Bayesian belief networks (BBN) based on the minimum description length (MDL) principle. First, we give a formula of description length based on which the MDL-based procedure learns a BBN. Secondly, we point out that the difference between the MDL-based and Cooper and Herskovits procedures is essentially in the priors rather than in the approa...
متن کامل