Most speech recognition systems search through large finite state machines to find the most likely path, or hypothesis. Efficient search in these large spaces requires pruning of some hypotheses. Popular pruning techniques include probability pruning which keeps only hypotheses whose probability falls within a prescribed factor from the most likely one, and rank pruning which keeps only a presc...

Designing a feed-forward neural network with optimal topology in terms of complexity (hidden layer nodes and connections between nodes) and training performance has been a matter of considerable concern since the very beginning of neural networks research. Typically, this issue is dealt with by pruning a fully interconnected network with “many” nodes in the hidden layers, eliminating “superfluo...

 Knowledge of the Saxaul dimensions used in sand dunes stabilization is considered essential for designing live windbreak in desert regions. This research aimed to collect and analysis data and was performed on the pruned and control shrubs of Haloxylon aphyllum L. in Yazd province, Iran in the last two decades. Our review clearly showed the superiority of shrubs pruned at the height of 35 cm i...

Recent empirical studies revealed two surprising pathologies of several common decision tree pruning algorithms. First, tree size is often a linear function of training set size, even when additional tree structure yields no increase in accuracy. Second, building trees with data in which the class label and the attributes are independent often results in large trees. In both cases, the pruning ...

Neural networks with memristive memory for weights have been proposed as an energy-efficient solution scaling up of neural network implementations. However, training such is still challenging due to various memristor imperfections and faulty elements. Such faults are becoming increasingly severe the density arrays increases in order scale weight memory. We propose fault pruning, a robust scheme...