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Machine Learning for Web Data
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wicked hard problem 10sof millions of URLs /day 100s of millions of events / day 1000s of millions of
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Entity disambiguation This isimportant. Company disambiguation is a very common problem – Are “Microsoft”, “Microsoft Corporation”, and “MS” the same company?
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Axioms of Probability 0≤ P(A) ≤ 1 P(True) = 1 P(False) = 0 P(A or B) = P(A) + P(B) – P(A and B)
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P(A or B)= P(A) + P(B) – P(A and B) P(A) P(B) P(A and B)
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Example • Population of10,000 • 1% have rare disease • There’s a test that is 99% effective. – 99% of sick patients test positive – 99% of healthy patients test negative
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Given a positivetest result, what is the probability that the patient is sick?
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Disease Diagnosis 99 sickpatients test positive, 99 healthy patients test positive Given a positive test, there is a 50% probability that the patient is sick.
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Bayesian Disease Know theprob. of testing sick given healthy, and healthy given sick Use Bayes theorem to invert probabilities
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1. Obtain Data “pointingand clicking does not scale!” http://www.delicious.com/pskomoroch/dataset
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4. Model Python • NLTK- http://www.nltk.org/ • Scikits Learn - http://scikit- learn.sourceforge.net/
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