ID3 계열의 귀납적 기계학습

  • 발행 : 1995.05.01

초록

키워드

참고문헌

  1. Machine Learning v.4 Supporting start-to-finish development of knowedge bases R.Bareiss;B.Porter;K.Murray
  2. Machine Learning: An Artificial Intelligence Approach An overview of machine learning J.G.Carbonell
  3. Technical Report ML-TR-29, Laboratory for Computer Science Research, Rutgers University Abductive explanation based learning : A solution to the multiple explanation problem W.Cohen
  4. Proceedings of the Sixth International Workshop on Machine Learning Finding new rules for incomplete theories : Explicit biases for induction with contextual information A.Danyluk
  5. Technical report, Columbia University Generation and evaluation of contextual heuristics for inductive learning A.Danyluk
  6. Machine Learning v.1 no.2 Explanation-based learning : An alternative view G.DeJong;R.Mooney
  7. Technical Report STAN-CS-81-891, Stanford University The role of the critic in learning systems T.G.Dietterich;B.G.Buchanan
  8. Artificial Intelligence v.40 Models of incremental concept formation J.Gennari;P.Langley;D.Fisher
  9. Machine Learning v.3 Learning by failing to explain : Using partial explanations to learn in incomplete or intractable domains R.Hall
  10. Artificial Intelligence v.45 Explanining and repairing plans that fail K.J.Hammond
  11. Machine Learning Journal Concept learning in context R.M.Keller
  12. Proceedings of the Fourth International Machine Learning Workshop What is an explanation in DISCIPLE? Y.Kodratoff;G.Tecuci
  13. Technical Report GIT-ICS-90/19, Georgia Institute of Technology An introduction to case-based reasoning J.Kolodner
  14. Technical Report GIT-ICS-88/34, Georgia Institute of Technology Design and implementation of a case memory J.Kolodner;R.Thau
  15. Artificial Intelligence v.33 no.1 Soar : An architecture for general intelligence J.E.Laired;A.Newell;P.S.Rosenbloom
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  17. Machine Learning : An Artificial Intelligence Approach v.I R.Michalski;J.Carbonell;T.Mitchell
  18. Machine Learning : An Artificial Intelligence Approach v.Ⅱ R.Michalski;J.Carbonell;T.Mitchell
  19. Machine Learning : An Artificial Intelligence Approach A theory and methodology of inductive inference R.S.Michalski
  20. Machine Learning An Artificial Intelligence Approach v.Ⅱ Understading the nature of learning : Issues and research directions R.S.Michalski
  21. Artificial Intelligence v.40 Explanation-based learning : A problem solving perspective S.Minton;J.G.Carbonell;C.A.Knoblock;D.R.Kuokka;O.Etzioni;Y.Gil
  22. Communcations of the ACM v.37 no.7 Experience with a learning personal assistant T.M.Mitchell;R.Caruana;D.Freitag;J.McDermott;D.Zabowski
  23. Machine Learning v.1 no.1 Explanation-based generalization : A unifying view T.M.Mitchell;R.M.Keller;S.T.Kedar-Cabelli
  24. Proceedings of the Fifth International Conference on Machine Learning ID5 : An incremental ID3 Utgoff,P.
  25. Machine Learning v.4 Incremental induction of decision trees Utgoff,P.
  26. Proceedings of the Eleventh International Conference on Machine Learning An improved algorithm for incremental induction of decision trees Utgoff,P.
  27. Machine Learning v.1 Induction of decision trees J.R.Quinlan
  28. Machine Learning A Multistrategy Approach v.Ⅳ Michalski,R.;Tecuci,G.
  29. Machine Learning : An Artificial Intelligence Approach Why should machines learn? H.A.Simon
  30. Machine Learning : Artificial Intelligence Approach v.Ⅲ Apprenticeship learning in imperfect domain theories G.Tecuci;Y.Kodratoff
  31. Machine Learning of Inductive Bias P.E.Utgoff
  32. Machine Learning : Artificial Intelligence Approach v.Ⅲ Kodratoff,Y.;Michalski,R.