참고문헌

1. Machine Learning v.4 Multistrategy Learning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms J.W.Bala(et al.);R.S.Michalski(ed.);G.Tecuci(ed.)
2. Machine Learning v.4 WHY : A System That Learns Using Causal Models and Examples C.Baroglio(et al.);R.S.Michalski(ed.);G.Tecuci(ed.)
3. Readings in Knowledge Acquisition and Learning B.G.Buchanan(ed.);D.C.Wilkions(ed.)
4. Machine Learning v.4 GEMINI : An Integration of Analytical and Empirical Learning A.P.Danyluk;R.S.Michalski(ed.);G.Tecuci(ed.)
5. IEEE Trans. on SMC v.23 Knowledge-based Connectionism for Revision Domain Theories L.M.Fu
6. Neural Networks in Computer Intelligence L.M.Fu
7. AAAI '92 COGIN : Symbolic Induction with Genetic Algorithms D.P.Greene;S.F.Smith
8. Artificial Intelligence and Neural Networks V.Honavar(ed.);L.Uhr(ed.)
9. Multistrategy Learning R.S.Michalski(ed.)
10. Machine Learning : A Multistrategy Approach v.Ⅳ R.S.Michalski(ed.);G.Tecuci(ed.)
11. Machine Learning v.11 Inferential Theory of Learning as a Conceptural Basis for Multistrategy Learning R.S.Michalski
12. Machine Learning v.4 Inferential Theory of Learning : Developing Foundations for Multistrategy Learning R.S.Michalski;R.S.Michalski(ed.);G.Tecuci(ed.)
13. Readings in Knowledge Acquisition and Learning Toward a unified theory of learning : Multistrategy task-adaptive learning R.S.Michalski;B.G.Buchanan(ed.);D.C.Wilkins(ed.)
14. Machine Learning v.1 Explanation-Based Generalization : A Unifying View T.M.Mitchell(et al.)
15. Machine Learning v.4 A Multistrategy Approach to Theory Refinement R.J.Mooney;D.Ourston;R.S.Michalski(ed.);G.Tecuci(ed.)
16. Machine Learning v.11 Balanced Cooperative Modeling K.Morik
17. Machine Learning v.11 Learning Causal Patterns : Making a Transition from Data-Driven to Theory-Driven Learning M.Pazzani
18. Machine Learning v.1 Induction of Decision Trees J.R.Quinlan
19. Readings in Machine Learning J.Shavlik(ed.);T.Dietterich(ed.)
20. Proc. of the 6th Int. Workshop on the Machine Learning Combining Explanation-Based Learning and Artificial neural Network J.W.Shavlik;G.G.Towell
21. Machine Learning v.11 Plausible Justification Trees : A Framework for Deep and Dynamic Integration of Learning Strategies G.Tecuci
22. Machine Learning v.4 An Inference-Based Frame-work for Multistrategy Learning G.Tecuci;R.S.Michalski(ed.);G.Tecuci(ed.)
23. CMU-CS-91-197 The Monk's Problems : A Performance Comparison of Different Learning Algorithms S.B.Thrun(et al.)
24. Machine Learning v.13 Extracting Refined Rules from knowledge-Based Neural Networks G.G.Towell;J.W.Shavlik
25. Machine Learning v.4 Improving a Rule Induction System Using Genetic Algorithms H.Vafaie;K.De Jong;R.S.Michalski(ed.);G.Tecuci(ed.)
26. Machine Learning v.4 Theory Completion Using Knowledge-based learning B.L.Whitehall;S.C.Lu;R.S.Michalski(ed.);G.Tecuci(ed.)