Chapter 8: Extensions and Applications

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Chapter 8 Extensions and Applications
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Learning from Massive Datasets

• Can it be held in main memory?---Naïve Byaes
  Method
• Some learning schemes are incremental some are
  not.
• What about time it takes to model?should be
  linear or near linear
• What to do when data set is too large?
• Use a small subset of data for training---law of
  diminishing returns
• Some schemes do better with more data but there
  is also a danger of overfitting
• Parallelization is another way---develop
  parallelized versions of learning schemes

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• Incorporating Domain Knowledge Metadata---data
  about data---semantic, causal, and functional
• Text and web mining
• Adversarial situations Junk email filtering, for
  example