Time Efficient Optimal Tunning through Various Learning Rules in Unify Computing (TEOTLRUC)

Computing Optimal Sequential Allocation Rules in Clinical Trials*

Michael N. Katehakis State University of New York at Stony Brook and Cyrus Derman Columbia University The problem of assigning one of several treatments in clinical trials is formulated as a discounted bandit problem that was studied by Gittins and Jones. The problem involves comparison of certain state dependent indices A recent characterization of the index is used to calculate more efficient...

Optimal Learning Rules for Dis rete Synapses

Globally Optimal On-line Learning Rules

We present a method for determining the globally optimal on-line learning rule for a soft committee machine under a statistical mechanics framework. This work complements previous results on locally optimal rules, where only the rate of change in generalization error was considered. We maximize the total reduction in generalization error over the whole learning process and show how the resultin...

Optimal Learning Rules for Discrete Synapses

There is evidence that biological synapses have a limited number of discrete weight states. Memory storage with such synapses behaves quite differently from synapses with unbounded, continuous weights, as old memories are automatically overwritten by new memories. Consequently, there has been substantial discussion about how this affects learning and storage capacity. In this paper, we calculat...

Optimal stopping rules for active learning

Active learning algorithms aim to minimise the amount of labelled data used to learn a target concept. However, there is no formal framework for expressing the trade-off between needed accuracy and the cost of label acquisition, rendering the objective evaluation of algorithms problematic and the development of criteria for deciding when to terminate data acquisition impossible. This paper aims...