참고문헌
- Machine Learning v.4 Supporting start-to-finish development of knowedge bases R.Bareiss;B.Porter;K.Murray
- Machine Learning: An Artificial Intelligence Approach An overview of machine learning J.G.Carbonell
- 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
- Proceedings of the Sixth International Workshop on Machine Learning Finding new rules for incomplete theories : Explicit biases for induction with contextual information A.Danyluk
- Technical report, Columbia University Generation and evaluation of contextual heuristics for inductive learning A.Danyluk
- Machine Learning v.1 no.2 Explanation-based learning : An alternative view G.DeJong;R.Mooney
- Technical Report STAN-CS-81-891, Stanford University The role of the critic in learning systems T.G.Dietterich;B.G.Buchanan
- Artificial Intelligence v.40 Models of incremental concept formation J.Gennari;P.Langley;D.Fisher
- Machine Learning v.3 Learning by failing to explain : Using partial explanations to learn in incomplete or intractable domains R.Hall
- Artificial Intelligence v.45 Explanining and repairing plans that fail K.J.Hammond
- Machine Learning Journal Concept learning in context R.M.Keller
- Proceedings of the Fourth International Machine Learning Workshop What is an explanation in DISCIPLE? Y.Kodratoff;G.Tecuci
- Technical Report GIT-ICS-90/19, Georgia Institute of Technology An introduction to case-based reasoning J.Kolodner
- Technical Report GIT-ICS-88/34, Georgia Institute of Technology Design and implementation of a case memory J.Kolodner;R.Thau
- Artificial Intelligence v.33 no.1 Soar : An architecture for general intelligence J.E.Laired;A.Newell;P.S.Rosenbloom
- Machine Learning v.1 Chunking in Soar : The anatomy of a general learning mechanism J.E.Laired;P.S.Rosenbloom;A.Newell
- Machine Learning : An Artificial Intelligence Approach v.I R.Michalski;J.Carbonell;T.Mitchell
- Machine Learning : An Artificial Intelligence Approach v.Ⅱ R.Michalski;J.Carbonell;T.Mitchell
- Machine Learning : An Artificial Intelligence Approach A theory and methodology of inductive inference R.S.Michalski
- Machine Learning An Artificial Intelligence Approach v.Ⅱ Understading the nature of learning : Issues and research directions R.S.Michalski
- 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
- 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
- Machine Learning v.1 no.1 Explanation-based generalization : A unifying view T.M.Mitchell;R.M.Keller;S.T.Kedar-Cabelli
- Proceedings of the Fifth International Conference on Machine Learning ID5 : An incremental ID3 Utgoff,P.
- Machine Learning v.4 Incremental induction of decision trees Utgoff,P.
- Proceedings of the Eleventh International Conference on Machine Learning An improved algorithm for incremental induction of decision trees Utgoff,P.
- Machine Learning v.1 Induction of decision trees J.R.Quinlan
- Machine Learning A Multistrategy Approach v.Ⅳ Michalski,R.;Tecuci,G.
- Machine Learning : An Artificial Intelligence Approach Why should machines learn? H.A.Simon
- Machine Learning : Artificial Intelligence Approach v.Ⅲ Apprenticeship learning in imperfect domain theories G.Tecuci;Y.Kodratoff
- Machine Learning of Inductive Bias P.E.Utgoff
- Machine Learning : Artificial Intelligence Approach v.Ⅲ Kodratoff,Y.;Michalski,R.