Word alignment play an important role in the training of statistical machine translation systems. We present a technique to refine word alignments at phrase level after the collection of sentences from the Kazakh-English parallel corpora. The estimation technique extracts the phrase pairs from the word alignment and then incorporates them into the translation system for further steps. Although ...

Individuals with developmental disabilities may fail to attend to multiple features in compound stimuli (e.g., arrays of pictures, letters within words) with detrimental effects on learning. Participants were 5 children with autism spectrum disorder who had low to intermediate accuracy scores (35% to 84%) on a computer-presented compound matching task. Sample stimuli were pairs of icons (e.g., ...

We discuss how to build sparse one hidden layer MLP replacing the standard l2 weight decay penalty on all weights by an l1 penalty on the linear output weights. We will propose an iterative two step training procedure where the output weights are found using FISTA proximal optimization algorithm to solve a Lasso-like problem and the hidden weights are computed by unconstrained minimization. As ...

In this paper, we investigate ways to learn efficiently from uncertain data using belief functions. In order to extract more knowledge from imperfect and insufficient information and to improve classification accuracy, we propose a supervised learning method composed of a feature selection procedure and a two-step classification strategy. Using training information, the proposed feature selecti...

Training algorithms for Multilayer Perceptions optimize the set of Wweights and biases, w, so as to minimize au error t%nction,E, applied to a set of N training patterns. The well-known back propagation algorithm combines an efficient method of estimating the gradient of the error function in weight space, AE=g, with a simple gradient descent procedure to adjust the weighb, Aw = –qg. More effic...