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Learning Based Java

Learning Based Java. The Basics MIAS DSSI. phrase 1. word 1. word 2. word 3. Assumptions. The user has an OO representation Only objects need to be classified. Classifiers. Any method that classifies an object … … and returns features Does not imply learning.

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Learning Based Java

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  1. Learning Based Java The Basics MIAS DSSI

  2. phrase1 word1 word2 word3 Assumptions • The user has an OO representation • Only objects need to be classified

  3. Classifiers • Any method that classifies an object … … and returns features • Does not imply learning discrete POSLabel(POSLabeledWord w) <- { return w.label; }

  4. Learning Classifiers • Learn to mimic an oracle • Using other classifiers as features • From data • With a learning algorithm • Can produce a discrete distribution

  5. John threwtheballtoMary . Inference • Making classifiers’ decisions coherent • Example: Shallow Parsing • Maximize E[correct decisions] Chunks cannot overlap

  6. Constraints • Written in First Order Logic • In terms of learning classifiers and Java objects forall (Phrase p in sentence) exists (Word w in p.allWords()) MyLearner(w) :: “yes!”

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