Abstract
We describe a graph-based induction algorithm that extracts typical patterns from colored digraphs. The method is shown to be capable of solving a variety of learning problems by mapping the different learning problems into colored digraphs. The generality and scope of this method can be attributed to the expressiveness of the colored digraph representation, which allows a number of different learning problems to be solved by a single algorithm. We demonstrate the application of our method to two seemingly different learning tasks: inductive learning of classification rules, and learning macro rules for speeding up inference. We also show that the uniform treatment of these two learning tasks enables our method to solve complex learning problems such as the construction of hierarchical knowledge bases.
Similar content being viewed by others
References
R.S. Michalski, “A theory and methodology of inductive learning,”Artif. Intell. vol. 20, pp. 111–161, 1983.
J.R. Quinlan, “Induction of decision trees,”Machine Learning vol. 1, pp. 81–106, 1986.
T. Ellman, “Explanation-Based Learning: A survey of programs and perspectives,”ACM Comput. Surv. vol. 21, no. 2, pp. 165–220, 1989.
S. Mahadevan and J. Connell, “Automatic programming of behavior-based robots using reinforcement learning,”Artif. Intell. vol. 55, pp. 311–365, 1992.
B. Falkenhainer and K.D. Forbus, “Compositional modeling: finding the right model for the job,”Artif. Intell. vol. 51, pp. 95–143, 1991.
Z.Y. Liu and A.M. Farley, “Shifting ontological perspectives in reasoning about physical systems,” inAAAI-90, Boston, MA, 1990, pp. 395–400.
M. Lebowitz, “Integrated learning: controlling explanation,”Cog. Sci. vol. 10, pp. 219–240, 1986.
M. Pazzani, M. Dyer, and M. Flowers,” The role of prior causal theories in generalization,” inAAAI-86, Philadelphia, PA, 1986, pp. 545–550.
A.P. Danyluk, “The use of explanation for similarity-based learning,” inIJCAI-87, Milan, Italy, 1987, pp. 274–276.
H. Hirsh, “Combining empirical and analytical learning with version spaces,” inML-89, Ithaca, NY, 1989, pp. 29–33.
P.S. Rosenbloom and J. Aasman, “Knowledge level and inductive uses of chunking (EBL),” inAAAI-90, Boston, MA, 1990, pp. 821–827.
O. Etzioni, “STATIC: A problem-space compiler for PRODIGY,” inAAAI-91, Anaheim, CA, 1991, pp. 533–540.
S.A. Vere, “Induction of relational productions in the presence of background information,” inIJCAI-77, Cambridge, MA, 1977, pp. 349–355.
J.R. Anderson and P.J. Kline, “A learning system and its psychological implications,” inIJCAI-79, Tokyo, Japan, 1979, pp. 16–21.
R. Levinson, “A self-organizing retrieval system for graphs,” inAAAI-84, Austin, TX, 1984, pp. 203–206.
N.S. Flann and T.G. Dietterich, “A study of explanation-based methods for inductive learning,”Machine Learning, 1989, pp. 187–226.
L.B. Holder, “Empirical substructure discovery,” inML-89, Ithaca, NY, 1989, pp. 133–136.
L.B. Holder, D.J. Cook, and H. Bunke, “Fuzzy substructure discovery,” inML-92, Aberdeen, Scotland, 1992, pp. 218–223.
T.M. Mitchell, R.M. Keller, and S.T. Kedar-Cabelli, “Explanation-based generalization: a unifying view,”Machine Learning vol. 1, pp. 47–80, 1986.
G. DeJong and R. Mooney, “Explanation-based learning: an alternative view,”Machine Learning vol. 1, pp. 145–176, 1986.
R.E. Korf, “Macro-operators: A weak method for learning,”Artif. Intell. vol. 25, pp. 35–77, 1985.
G.A. Iba, “A heuristic approach to the discovery of macro-operators,”Machine Learning vol. 3, pp. 285–317, 1989.
B. Kuipers, “Qualitative simulation,”Artif. Intell. vol. 29, pp. 289–338, 1986.
G.G. Towell, J.W. Shavlik, and M.O. Noordewier, “Refinement of approximate domain theories by knowledge-based neural networks,” inAAAI-90, Boston, MA, 1990, pp. 861–866.
B. Efron, “The jackknife, the bootstrap and other resampling plans,” inSIAM, Bowling Green State University: Bowling Green, OH, 1982.
L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone,Classification and Regression Trees Wadsworth & Brooks/Cole Advanced Books & Software: Belmont, CA, 1984.
S.M. Weiss and N. Indurkhya, “Reduced complexity rule induction,” inIJCAI-91, Sydney, Australia, 1991, pp. 678–684.
S. Yamada and S. Tsuji, “Selective learning of macro-operators with perfect causality,” inIJCAI-89. Detroit, MI, 1989, pp. 603–608.
C. Mead and L. Conway,Introduction to VLSI Systems Addison-Wesley: Reading, MA, 1980.
S. Arikawa, S. Miyano, and A. Shinohara, “Knowledge acquisition from amino acid sequences by learning algorithms,” inJKAW92, Hatoyama, Japan, 1992, pp. 109–128.
S. Minton, “Quantitative results concerning the utility of Explanation-Based Learning,”Artif. Intell. vol. 42, pp. 363–391, 1990.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Yoshida, K., Motoda, H. & Indurkhya, N. Graph-based induction as a unified learning framework. Appl Intell 4, 297–316 (1994). https://doi.org/10.1007/BF00872095
Issue Date:
DOI: https://doi.org/10.1007/BF00872095