Context Maximizing : Finding MDL Decision Trees

Stochastic complexity in learning

This is an expository paper on the latest results in the theory of stochastic complexity and the associated MDL principle with special interest in modeling problems arising in machine learning. As an illustration we discuss the problem of designing MDL decision trees, which are meant to improve the earlier designs in two ways: First, by use of the sharper formula for the stochastic complexity a...

Decision-tree backing-off in HMM-based speech synthesis

This paper proposes a decision-tree backing-off technique for an HMM-based speech synthesis system. In the system, a decision-tree based context clustering technique is used for constructing parameter tying structures. In the context clustering, the MDL criterion has been used as a stopping criterion. In this paper, however, huge decision-trees are constructed without any stopping criterion. In...

Learning MDL-guided Decision Trees for Constructor-Based Languages

Learning MDL-guided Decision Trees for Constructor-Based Languages

Pruning Regression Trees with MDL

Pruning is a method for reducing the error and complexity of induced trees. There are several approaches to pruning decision trees, while regression trees have attracted less attention. We propose a method for pruning regression trees based on the sound foundations of the MDL principle. We develop coding schemes for various constructs and models in the leaves and empirically test the new method...