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...

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 ...

Near-term quantum hardware can support two-qubit operations only on the qubits that interact with each other. Therefore, to execute an arbitrary circuit hardware, compilers have first perform task of qubit routing, i.e., transform either by inserting additional SWAP gates or reversing existing CNOT satisfy connectivity constraints target topology. The depth transformed circuits is minimized uti...

We consider an application of combinatorial search to the optimization topologies in series-parallel networks. propose a recursive over space decomposition trees, which partial solutions are obtained by exploring k-way partitionings expandable nodes. present two complementary pruning techniques that bound value intermediate from above and below, applying monotonic operations contents unresolved...

Deep neural networks have achieved remarkable advancement in various intelligence tasks. However, the massive computation and storage consumption limit applications on resource-constrained devices. While channel pruning has been widely applied to compress models, it is challenging reach very deep compressions for such a coarse-grained structure without significant performance degradation. In th...

The superior performance of the recent deep learning models comes at cost a significant increase in computational complexity, memory use, and power consumption. Filter pruning is one effective neural network compression techniques suitable for model deployment modern low-power edge devices. In this paper, we propose loss-aware filter Magnitude Similarity based Variable rate Pruning (MSVFP) tech...