Several methods for reducing the running time of support vector machines (SVMs) are compared in terms of speed-up factor and classification accuracy using seven large real world datasets obtained from the UCI Machine Learning Repository. All the methods tested are based on reducing the size of the training data that is then fed to the SVM. Two probabilistic methods are investigated that run in linear time with respect to the size of the training data: blind random sampling and a new method for guided random sampling (Gaussian Condensing). These methods are compared with k-Nearest Neighbour methods for reducing the size of the training set and for smoothing the decision boundary. For all the datasets tested blind random sampling gave the best results for speeding up SVMs without significantly sacrificing classification accuracy.
We present the MultiScript Phonetic Search algorithm to address the problem of language learners looking up unfamiliar words that they heard. We apply it to Arabic dictionary lookup with noisy queries done using both the Arabic and Roman scripts. Our algorithm is based on a computational phonetic distance metric that can be optionally machine learned. To benchmark our performance, we created the ArabScribe dataset, containing 10,000 noisy transcriptions of random Arabic dictionary words. Our algorithm outperforms Google Translate's "did you mean" feature, as well as the Yamli smart Arabic keyboard.
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