A MINSAT Approach for Learning in Logic Domains

References

• Bennett K. P., Mangasarian O. L. Robust linear programming discrimination of two linearlyinseparable sets. Optimization Methods and Software (1992) 1:23–34Crossref, Google Scholar
• Bhargava H. K. Data mining bydecomposition: adaptive search for hypothesis generation. INFORMS Journal on Computing (1999) 3:239–247Link, Google Scholar
• Boros E., Hammer P. L., Ibaraki T., Kogan A., Mayoraz E., Muchnik I. An implementation of logical analysis of data. RUTCOR Research Report (1996) 29–96Rutgers University, NJGoogle Scholar
• Boros E., Ibaraki T., Makino K. Logical analysis of binary data with missing bits. Artificial Intelligence (1999) 107:219–263Crossref, Google Scholar
• Bradley P. S., Fayyad U. M., Mangasarian O. L. Mathematical programming for data mining: formulations and challenges. INFORMS Journal on Computing (1999) 3:217–238Link, Google Scholar
• Breiman L. Stacked regressions. Machine Learning (1996a) 24:49–64Crossref, Google Scholar
• Breiman L. Bias, variance, and arcing classifiers. (1996b) . Technical Report 460, Statistics Department, Universityof California, Berkeley, CAGoogle Scholar
• Breiman L., Friedman J. H., Olshen R. A., Stone C. J.Classification and Regression Trees (1984) (Wadsworth International Group, Belmont, CA) Google Scholar
• Cohen W. W. Pac-learning non-recursive Prolog clauses. Artificial Intelligence (1995) 79:1–38Crossref, Google Scholar
• Crama Y., Hammer P. L., Ibaraki T. Cause-effect relationships and partially defined Boolean functions. Annals of Operations Research (1988) 16:299–325Crossref, Google Scholar
• Domany E., vanHemmen J. L., Schulten K.Models of Neural Networks (1991) (Springer-Verlag, Berlin, Germany) Crossref, Google Scholar
• Felici G.Il Problema di riconoscimento automatico: proprietà ed algoritmi di soluzione (1995) (Tesi di Dottorato, Biblioteca Nazionale di Roma, Italy) Google Scholar
• Felici G., Truemper K.Learning Logic (1997) 450IASI Technical ReportGoogle Scholar
• Felici G., Sun F. S., Truemper K. A Method for controlling errors in two-class classifications. COMP99: 23rd Annual International Computer Software and Applications Conference (1999) Phoenix, Arizona, IEEE Computer Society, Los Alamitos, CA:186–191Crossref, Google Scholar
• Freed N., Glover F. Simple but powerful goal programming models for discriminant problems. European Journal of Operational Research (1981) 7:44–60Crossref, Google Scholar
• Freed N., Glover F. Evaluating alternative linear programming models to solve the two-group discriminant problem. Decision Sciences (1986) 17:151–162Crossref, Google Scholar
• Garey M. R., Johnson D. S.Computers and Intractability: A Guide to the Theoryof NP-Completeness (1979) (Freeman, San Francisco, CA) Google Scholar
• Gennari J. H., Langley P., Fisher D. Models of incremental concept formation. Artificial Intelligence (1989) 40:11–61Crossref, Google Scholar
• Golea M., Vitányi P. Average case analysis of a learning algorithm for μ DNF expressions. Computational Learning Theory (1995) (Springer-Verlag, Berlin) 342–356Proceedings of the Second European Conference EuroCOLT '95, Barcelona, SpainCrossref, Google Scholar
• Harrison D., Rubinfeld D. L. Hedonic prices and the demand of clean air. Journal of Environment Economics and Management (1978) 5:111–143Crossref, Google Scholar
• Hertz J., Krogh A., Palmer R. G.Introduction to the Theoryof Neural Computation (1991) (Addison Wesley, Redwood City, CA) Google Scholar
• Holte R. C. Verysimple classification rules perform well on most commonlyused datasets. Machine Learning (1993) 11:63–91Crossref, Google Scholar
• Hooker J. N.Optimization Methods for Logical Inference (1999) (WileyInterscience, New York) Google Scholar
• Kamath A. P., Karmarkar N. K., Ramakrishnan K. J., Resende M. G. C. A continuous approach to inductive inference. Mathematical Programming (1992) 57:215–238Crossref, Google Scholar
• Lam L., Suen C. Y. Application of majority voting to pattern recognition: an analysis of its behavior and performance. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans (1997) 27:553–567Crossref, Google Scholar
• Leibniz System (1996) . Version 4.3. Leibniz Company, Plano, TXGoogle Scholar
• Makino K., Yano K., Ibaraki T. Positive and Horn decomposabilityof partiallydefined Boolean functions. Discrete Applied Mathematics (1997) 74:251–274Crossref, Google Scholar
• Mangasarian O. L. Mathematical programming in neural networks. ORSA Journal on Computing (1993) 5:349–360Link, Google Scholar
• Mangasarian O. L., Setiono R., Wolberg W. H., Coleman T. F., Li Y. Pattern recognition via linear programming: theoryand application to medical diagnosis. Large-Scale Numerical Optimization (1990) (SIAM Publications, Philadelphia, PA) 22–30Google Scholar
• Mangasarian O. L., Street W. N., Wolberg W. H. Breast cancer diagnosis and prognosis via linear programming. Operations Research (1995) 43:570–577Link, Google Scholar
• Mangasarian O. L., Wolberg W. H. Cancer diagnosis via linear programming. SIAM News (1990) 23:1–18Google Scholar
• Muggleton S. Inductive logic programming: issues, results and the challenge of learning languages in logic. Artificial Intelligence (1999) 114:289–296Crossref, Google Scholar
• Murphy P. M., Aha D. W.UCI Repositoryof Machine Learning Databases: Machine Readable Data Repository (1994) (Department of Computer Science, Universityof California, Irvine, CA) Google Scholar
• Murthy S. K., Kasif S., Salzberg S. A system for induction of oblique decision trees. Journal of Artificial Intelligence Research (1994) 21:1–32Crossref, Google Scholar
• Nelson M. M., Illingworth W. T.A Practical Guide to Neural Nets (1990) (Addison-Wesley, Reading, MA) Google Scholar
• Quinlan R. Combining instance-based and model-based learning. Proceedings of the Tenth International Conference on Machine Learning (1993) Universityof Massachusetts, Amherst, MA, Morgan Kaufmann, San Mateo, CA:236–243Crossref, Google Scholar
• Shavlik J. W., Mooney R. J., Towell G. G. Symbolic and neural learning algorithms: an experimental comparison. Machine Learning (1991) 6:111–143Crossref, Google Scholar
• Thrun S. B., Bala J., Bloedorn E., Bratko I., Cestnik B., Cheng J., De Jong K., Džeroski S., Fahlman S. E., Fisher D. H., Hamann R., Kaufmann K., Keller S., Kononenko I., Kreuziger J., Michalski R. S., Mitchell T., Pachowics P., Reich Y., Vafaie H., Van de Welde W., Wenzel W., Wnek J., Zhang J. The MONK's problems—a performance comparison of different learning algorithms. (1991) . Technical Report CS-CMU-91-197, School of Computer Science, Computer Science Department, Carnegie Mellon University, Pittsburgh, PAGoogle Scholar
• Triantaphyllou E., Soyster L. On the minimum number of logical clauses inferred from examples. Computers and Operations Research (1996) 21:783–799Crossref, Google Scholar
• Triantaphyllou E., Allen L., Soyster L., Kumara S. R. T. Generating logical expressions from positive and negative examples via a branch-and-bound approach. Computers and Operations Research (1994) 21:185–197Crossref, Google Scholar
• Truemper K.Effective Logic Computation (1998) (Wiley-Interscience, New York) Google Scholar
• Vailant L. G. Learning disjunctions and conjunctions. Proceedings of the 9th IJCAI (1985) 550–556Google Scholar
• Wolpert D. H. Stacked generalization. Neural Networks (1992) 5:241–259Crossref, Google Scholar