Many researchers have shown that ensemble methods such as Boosting and Bagging improve the accuracy of classification. Boosting and Bagging perform well with unstable learning algorithms such as neural networks or decision trees. Pruning decision tree classifiers is intended to make trees simpler and more comprehensible and avoid over-fitting. However it is known that pruning individual classif...

Improved efficiency of pruning accelerates the search process and leads to a more time efficient speech recognition system. The goal of this work was to develop a new pruning technique which optimizes the well known probability-based pruning (beam width) by utilization of confidence measurement. We use normalized hypotheses scores to guide the beam width of the pruning process dynamically frame...

The default multilayer neural network topology is a fully interlayer connected one. This simplistic choice facilitates the design but it limits the performance of the resulting neural networks. The best-known methods for obtaining partially connected neural networks are the so called pruning methods which are used for optimizing both the size and the generalization capabilities of neural networ...

abstract the quantative and qualitative yield comparison of three cucumber f1 cultivars including' sina, amyral, and negin was evaluated using 3×3×3 factorial experimental design with six replication. three types of pruning treatments were applied; cutting down all branches on main stem (b1), leaving one node and the leaf next to it on all branches (b2), leaving two node and the leaf next to th...

In this paper, a generalized output pruning algorithm for matrixvector multiplication is proposed first. Then the application of the proposed pruning algorithm to compute pruning Discrete Cosine Transform (DCT) is addressed. It is shown that, for a given decomposition of the matrix of the transform kernel and the pruning pattern, the unnecessary operations for computing an output pruning DCT ca...

The Prism family of algorithms induces modular classification rules in contrast to the Top Down Induction of Decision Trees (TDIDT) approach which induces classification rules in the intermediate form of a tree structure. Both approaches achieve a comparable classification accuracy. However in some cases Prism outperforms TDIDT. For both approaches pre-pruning facilities have been developed in ...

Axon pruning is widely used for the refinement of neural circuits in both vertebrates and invertebrates, and may also contribute to the pathogenesis of neurodegenerative diseases. However, little is known about the cellular and molecular mechanisms of axon pruning. We use the stereotyped pruning of gamma neurons of the Drosophila mushroom bodies (MB) during metamorphosis to investigate these me...

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...