Mahdi Dibaiee
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b26347e19f
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feat(notmnist): notmnist example using SGD + learning rate decay
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2016-09-10 00:36:15 +04:30 |
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Mahdi Dibaiee
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891f48a2d0
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feat(topten): top-ten classification with evenly distrubuted data
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2016-08-21 00:59:42 +04:30 |
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Mahdi Dibaiee
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eebf5e0222
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feat(verbose): print more information using -v or --verbose flags
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2016-08-08 12:35:26 +04:30 |
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Mahdi Dibaiee
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099c25e166
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feat(stopwords): removeWords and removeStopwords functions as pre-processors
feat(confidence, WIP): calculate confidence of each classification
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2016-08-08 10:02:26 +04:30 |
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Mahdi Dibaiee
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ea1f05f001
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fix(naivebayes): fix the algorithm to make it actually work
feat(cleanDocuments): preprocess documents, use stemming and stopword elimination for better accuracy
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2016-08-05 23:54:36 +04:30 |
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Mahdi Dibaiee
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3cf0625794
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fix(precision): little bug in implementation
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2016-07-30 16:52:34 +04:30 |
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Mahdi Dibaiee
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76e7e7faef
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fix(recall, precision): little bug in calculations
feat(fmeasure): calculate fmeasure using recall and precision
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2016-07-29 22:09:30 +04:30 |
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Mahdi Dibaiee
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b5b4629318
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feat(results): accuracy, recall and precision functions used to calculate measures
fix: read data from another repository
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2016-07-29 17:55:59 +04:30 |
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Mahdi Dibaiee
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26eb4531fa
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feat(naivebayes): implement NaiveBayes algorithm
feat(example): a document classifier using NaiveBayes over reuters data
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2016-07-29 16:16:44 +04:30 |
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