Pruning a decision tree is considered by some researchers to be the most important part of tree building in noisy domains. While, there are many approaches to pruning, an alternative approach of averaging over decision trees has not received as much attention. We perform an empirical comparison of pruning with the approach of averaging over decision trees. For this comparison we use a computa-t...

Many of the pruning strategies used to remove less likely hypotheses from the search beam in large vocabulary speech recognition (LVR) systems, have a peak search space many times greater than the average search space. This paper discusses two such pruning strategies used within BT’s speech recognition architecture [1], Step pruning and Histogram pruning. Two-tier pruning is proposed as a simpl...

A novel, patented topping power cycle is described that takes its energy from a very high-temperature heat source and in which the temperature of the heat sink is still high enough to operate another, conventional power cycle. The top temperature heat source is used to evaporate a low saturation pressure liquid, which serves as the driving fluid for compressing the secondary fluid in an ejector...

Radial Basis Function Neural Networks are well suited for learning the system dynamics of a robot manipulator and implementation of these networks in the control scheme for a manipulator is a good way to deal with the system uncertainties and modeling errors which often occur. The problem with RBF networks however is to find a network with suitable size, not too computational demanding and able...

The Multi-model search framework generalizes minimax to allow exploitation of recursive opponent models. In this work we consider adding pruning to the multi-model search. We prove a sufficient condition that enables pruning and describe two pruning algorithms, αβ∗ and αβ∗ 1p. We prove correctness and optimality of the algorithms and provide an experimental study of their pruning power. We show...

Introduction Bonferroni Pruning Pruning is a common technique to avoid over tting in decision trees. Most pruning techniques do not account for one important factor | multiple comparisons. Multiple comparisons occur when an induction algorithm examines several candidate models and selects the one that best accords with the data. Making multiple comparisons produces incorrect inferences about mo...