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kNN classifier is widely used in text categorization, however, kNN has the large computational and store requirements, and its performance also suffers from uneven distribution of training data. Usually, condensing technique is resorted to reducing the noises of training data and decreasing the cost of time and space. Traditional condensing technique picks up samples in a random manner when initialization. Though random sampling is one means to reduce outliers, the extremely stochastic may lead to bad performance sometimes, that is, advantages of sampling may be suppressed. To avoid such a misfortune, we propose a variation of traditional condensing technique. Experiment results illustrate this strategy can solve above problems effectively.

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Automatic restoration of punctuation from unpunctuated text has application in improving the fluency and applicability of speech recognition systems. We explore the possibility that syntactic information can be used to improve the performance of an HMM-based system for restoring punctuation (specifically, commas) in text. Our best methods reduce sentence error rate substantially-by some 20%, with an additional 8% reduction possible given improvements in extraction of the requisite syntactic information. 1 In an old unattributed joke, an English professor asks some students to punctuate the word sequence "Woman without her man is nothing". The male students preferred "Woman, without her man, is nothing." whereas the female proposed "Woman! Without her, man is nothing." No, it's not funny, but it does make the point.