Hierarchies of Adaptive Experts

TD Learning of Game Evaluation Functions with Hierarchies of Adaptive Experts

Adaptively Growing Hierarchical Mixtures of Experts

We propose a novel approach to automatically growing and pruning Hierarchical Mixtures of Experts . The constructive algorithm proposed here enables large hierarchies consisting of several hundred experts to be trained effectively. We show that HME's trained by our automatic growing procedure yield better generalization performance than traditional static and balanced hierarchies. Evaluation of...

Growing Hierarchical Mixtures of Experts

We propose a novel approach to automatically growing and pruning Hierarchical Mixtures of Experts. The constructive algorithm proposed here enables large hierarchies consisting of several hundred experts to be trained e ectively. We show that HME's trained by our automatic growing procedure yield better generalization performance than traditional static and balanced hierarchies. Evaluation of t...

A New Approach of Learning Hierarchy Construction Based on Fuzzy Logic

In recent years, adaptive learning systems rely increasingly on learning hierarchy to customize the educational logic developed in their courses. Most approaches do not consider that the relationships of prerequisites between the skills are fuzzy relationships. In this article, we describe a new approach of a practical application of fuzzy logic techniques to the construction of learning hierar...

On Adaptive DLOGTIME and POLYLOGTIME Reductions

We investigate properties of the relativized AC and NC hierarchies in their DLOGTIME-, respectively, ALOGTIME-uniform setting and show that these hierarchies can be characterized in terms of adaptive reducibility in deterministic (poly)logarithmic time, i.e. in time O(log n)’ for i 2 0. Using this characterization, we substantially generalize various previous results concerning the structure of...