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Classification is the one of the most important techniques in Datamining for data analysis. In Datamining, different Classification Techniques are available to predict outcome for a given dataset. There are many classification techniques for predicting and estimating accuracy, one such famous technique is Naïve Bayes Classifier. Naïve Bayes is very popular as it is easy to build, not so complex and...
In this paper, we suggest to use a modified version of Smoothed-lscr0 (SL0) algorithm in the sparse representation step of iterative dictionary learning algorithms. In addition, we use a steepest descent for updating the non unit column-norm dictionary instead of unit column-norm dictionary. Moreover, to do the dictionary learning task more blindly, we estimate the average number of active atoms in...
The problem of estimating the parameters of an n-gram language model is a typical problem of estimating small probabilities. So far, two methods have been proposed and used to handle this problem: 1. the empirical Bayes method resulting in the Turing-Good estimates. Theses estimates do not have any constraints and tend to be very noisy. 2. discounting models like absolute (or linear) discounting....
The heuristic estimates of conditional phrase translation probabilities are based on frequency counts in a word-aligned parallel corpus. Earlier attempts at more principled estimation using Expectation-Maximization (EM) under perform this heuristic. This paper shows that a recently introduced novel estimator based on smoothing might provide a good alternative. When allphrasepairs are estimated (no...
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