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Machine Learning Chapter 10. Learning Sets of Rules Tom M

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3 Sequential Covering Algorithm SEQUENTIAL- COVERING (Target attribute; Attributes; Examples; Threshold)  Learned rules  {}  Rule  LEARN-ONE- RULE(Target_attribute, Attributes, Examples)  while PERFORMANCE (Rule, Examples) > Threshold, do –Learned_rules  Learned_rules + Rule –Examples  Examples – {examples correctly classified by Rule } –Rule  LEARN-ONE- RULE ( Target_attribute, Attributes, Examples ) –Learned_rules  sort Learned_rules accord to PERFORMANCE over Examples –return Learned_rules
Machine Learning Chapter 10. Learning Sets of Rules Tom M
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Machine Learning Chapter 10. Learning Sets of Rules Tom M
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Machine Learning Chapter 10. Learning Sets of Rules Tom M
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Machine Learning Chapter 10. Learning Sets of Rules Tom M
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Machine Learning Chapter 10. Learning Sets of Rules Tom M
Machine Learning Chapter 10. Learning Sets of Rules Tom M
Machine Learning Chapter 10. Learning Sets of Rules Tom M
Machine Learning Chapter 10. Learning Sets of Rules Tom M
[Tom M. Mitchell] on . *FREE* shipping on qualifying offers. Machine Learning
Machine Learning Chapter 10. Learning Sets of Rules Tom M
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Machine Learning Chapter 10. Learning Sets of Rules Tom M
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