Algorithmic Learning Theory

Algorithmic Learning Theory

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Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Algorithmic learning theory is a framework for machine learning. The framework was introduced in E. Mark Gold's seminal paper "Language identification in the limit." The objective of language identification is for a machine running one program to be capable of developing another program by which any given sentence can be tested to determine whether it is "grammatical" or "ungrammatical." The language being learned need not be English or any other natural language - in fact the definition of "grammatical" can be absolutely anything known to the tester. In the framework of algorithmic learning theory, the tester gives the learner an example sentence at each step, and the learner responds with a hypothesis, which is a suggested program to determine grammatical correctness. It is required of the tester that every possible sentence appears in the list eventually, but no particular order is required. It is required of the learner that at each step the hypothesis must be correct for all the sentences so far.
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Product details

  • Paperback | 92 pages
  • 152 x 229 x 6mm | 145g
  • United States
  • English
  • 6136597071
  • 9786136597072