Machine learning

Vishnu Nath, Stephen E. Levinson

Research output: Chapter in Book/Report/Conference proceedingChapter


Whenever a problem seems extremely open ended with a large variety of random variables that have an effect on the process, it is impossible for a human programmer to be able to account for every single case. The number of cases increases dramatically with an additional parameter. In such scenarios, probabilistic algorithms have the greatest applicability. In such scenarios, the algorithms need to be given a couple of examples of scenarios it might come across and the algorithm would be able to handle a new scenario with reasonable accuracy. The key word in the previous statement is ‘reasonable’. There is no probabilistic algorithm that will always return the optimum result with a probability of 1. That would make it a deterministic algorithm which, as has just been discussed, cannot handle every potential case. In this chapter, we discuss the algorithms that were employed to successfully complete the experiment.

Original languageEnglish (US)
Title of host publicationSpringerBriefs in Computer Science
Number of pages6
StatePublished - 2014
Externally publishedYes

Publication series

NameSpringerBriefs in Computer Science
ISSN (Print)2191-5768
ISSN (Electronic)2191-5776


  • High Reward
  • Label Data
  • Reinforcement Learning Algorithm
  • Unlabeled Data
  • Unsupervised Learning

ASJC Scopus subject areas

  • Computer Science(all)


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