CSE P573 Applications of Artificial Intelligence Bayesian Learning - PowerPoint PPT Presentation

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CSE P573 Applications of Artificial Intelligence Bayesian Learning

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CSE P573 Applications of Artificial Intelligence Bayesian Learning Henry Kautz Autumn 2004 Naive Bayes Classifier Important special, simple of a Bayes optimal ... – PowerPoint PPT presentation

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Title: CSE P573 Applications of Artificial Intelligence Bayesian Learning


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CSE P573Applications of Artificial
IntelligenceBayesian Learning
  • Henry Kautz
  • Autumn 2004

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Classify instance D as
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Naive Bayes Classifier
  • Important special, simple of a Bayes optimal
    classifier, where
  • hypothesis classification
  • all attributes are independent given the class

class
attrib. 1
attrib. 3
attrib. 2
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Expectation-Maximization
  • Consider learning a naïve Bayes classifier using
    unlabeled data. How can we estimate e.g. P(AC)?
  • Initialization randomly assign numbers to P(C),
    P(AC), P(BC)
  • repeat
  • E-step Compute P(CA,B)
  • M-step Re-compute maximum likelihood
    estimation of P(C), P(AC), P(BC)
  • Calculate log likelihood of data
  • until (likelihood of data not improving)

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Expectation-Maximization
  • Initialization randomly assign numbers to P(C),
    P(AC), P(BC).

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Expectation-Maximization
  • E-step Compute P(CA,B)

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Expectation-Maximization
  • M-step Re-compute maximum likelihood estimation
    of P(C), P(AC), P(BC)

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Expectation-Maximization
  • Calculate log likelihood of data

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EM Demo
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