Abstract
Machine learning is a very active sub-field of artificial intelligence concerned with the development of computational models of learning. Machine learning is inspired by the work in several disciplines: cognitive sciences, computer science, statistics, computational complexity, information theory, control theory, philosophy, and biology. Simply speaking, machine learning is learning by machine. From a computational point of view, machine learning refers to the ability of a machine to improve its performance based on previous results. From a biological point of view, machine learning is the study of how to create computers that will learn from experience and modify their activity based on that learning as opposed to traditional computers whose activity will not change unless the programmer explicitly changes it.
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References
Breiman, L., Friedman, J., Olshen, R. A. and Stone, P. J., 1984, Classification and Regression Trees, Wadsworth, Belmont, CA.
Breiman, L., 1996, Bagging predictors, Machine Learn. 24:123–140.
Cheng, J., Greiner, R., Kelly, J., Bell, D. A. and Liu, W., 2002, Learning Bayesian networks from data: an information-theory based approach, Artif. Intell. 137:43–90.
Dietterich, T. G., 1997, Machine-learning research: four current directions, AI Magazine 18:97–136.
Domingos, P. and Pazzani, M, 1996, Beyond independence: conditions for the optimality of the simple Bayesian classifier, in: Proc. 13th Int. Conf. on Machine Learning, L. Saitta, ed., Morgan Kaufmann, San Mateo, CA, pp. 105–112.
Elkan, C., 1997, Boosting and naive Bayesian learning, Technical Report, Department of Computer Science and Engineering, University of California.
Feigenbaum, E. A., 1961, The simulation of verbal learning behavior, in: Proc. Western Joint Computer Conf., pp. 121–131.
Fogel, L. J., Owens, A. J. and Walsh, M. J., 1966, Artificial Intelligence Through Simulated Evolution, Wiley, New York.
Geman, S., Bienenstock, E. and Doursat, R, 1992, Neural networks and the bias/variance dilemma, Neural Comput. 4:1–58.
Hebb, D. O., 1949, The Organization of Behavior: A Neurophysiological Theory, Wiley, New York.
Heckerman, D., 1998, A tutorial on learning with Bayesian networks, in: Learning in Graphical Models, M. I. Jordan, ed., Kluwer, Dordrecht.
Hopfield, J. J., 1982, Neural networks and physical systems with emergent collective computational abilities, Proc. Natl Acad. Sci. USA 79:2554–2558.
Hopfield, J. J. and Tank, D. W., 1985, Neural computation of decisions in optimization problems, Biol. Cybernet. 52:141–152.
Hunt, E. B., Marin, J. and Stone, P. T., 1966, Experiments in Induction, Academic, New York.
Kaelbling, L. P., Littman, M. L. and Moore, A. W., 1996, Reinforcement learning: a survey, J. Artif. Intell. Res. 4:237–285.
Kodratoff, Y. and Michalski, R. S., eds, 1990, Machine Learning—An Artificial Intelligence Approach, Vol. 3, Morgan Kaufmann, San Mateo, CA.
Langley, P., 1996, Elements of Machine Learning, Morgan Kaufmann, San Mateo, CA.
Langley, P. and Simon, H., 1995, Applications of machine learning and rule induction, Commun. ACM 38:54–64.
Lavrač, N. and Džeroski, S., 1994, Inductive Logic Programming: Techniques and Applications, Ellis Horwood, Chichester.
Michalski, R. S., Carbonell, J. G. and Mitchell, T. M., eds, 1983, Machine Learning—An Artificial Intelligence Approach, Vol. 1, Morgan Kaufmann, San Mateo, CA.
Michalski, R. S., Carbonell, J. G. and Mitchell, T. M., eds, 1986, Machine Learning—An Artificial Intelligence Approach, Vol. 2, Morgan Kaufmann, San Mateo, CA.
McCulloch, W. S. and Pitts, W., 1943, A logical calculus of the ideas immanent in nervous activity, Bull. Math. Biophys. 5:115–137.
Michie, D., Spiegelhalter, D. J. and Taylor, C. C., 1994, Machine Learning, Neural and Statistical Classification, Ellis Horwood, London.
Minsky, M. L. and Papert, S., 1969, Perceptrons: An Introduction to Computational Geometry, MIT Press, Cambridge, MA.
Mitchell, T. M., 1997, Machine Learning, McGraw-Hill, New York.
Muggleton, S. H., 1995, Inverse entailment and progol, New Generation Comput. (Special issue on Inductive Logic Programming) 13:245–286.
Muggleton, S. H. and Buntine, W., 1988, Machine invention of first-order predicates by inverting resolution, in: Proc. 5th Int. Conf. on Machine Learning, Morgan Kaufmann, San Mateo, CA, pp. 339–352.
Quinlan, J. R., 1986, Introduction to decision tree, Machine Learn. 1:81–106.
Quinlan, J. R., 1990, Learning logical definitions from relations, Machine Learn. 5:239–266.
Quinlan, J. R., 1993, C4.5: Programs for Machine Learning, Morgan Kaufmann, San Mateo, CA.
Rumelhart, D. E., Hinton, G. E. and Williams, R. J., 1986, Learning internal representations by error propagation, in: Parallel Distributed Processing: Explorations in the Microstructures of Cognition, Vol. 1, D. E. Rumelhart and J. L. McClelland, eds, MIT Press, Cambridge, MA, pp. 318–362.
Rosenblatt, F., 1962, Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms, Spartan, Chicago, IL.
Rumelhart, D. E. and McClelland, J. L., eds, 1986, Parallel Distributed Processing: Explorations in the Microstructures of Cognition, MIT Press, Cambridge, MA.
Russell, S. and Norvig, P., 2002, Artificial Intelligence: A Modern Approach, Prentice-Hall, Englewood Cliffs, NJ.
Samuel, A. L., 1959, Some studies in machine learning using the game of checkers, IBM J. Res. Dev. 3:210–229.
Schapire, R. E., 1990, The strength of weak learnability, Machine Learn. 5:197–227.
Shavlik, J. and Dietterich, T. (eds), 1990, Readings in Machine Learning, Morgan Kaufmann, San Mateo, CA.
Schwefel, H.-P., 1981, Numerical Optimization of Computer Models, Wiley, Chichester.
Schwefel, H.-P., 1995, Evolution and Optimum Seeking, Wiley, New York.
Stone, M., 1974, Cross-validatory choice and assessment of statistical predictions, J. R. Statist. Soc. 36:111–147.
Sutton, R. S. and Barto, A. G., 1998, Reinforcement Learning: An Introduction, MIT Press, Cambridge, MA.
Turing, A., 1950, Computing machinery and intelligence, Mind 59:433–460.
Vapnik, V. N., 1995, The Nature of Statistical Learning Theory, Springer, New York.
Wolpert, D. H. and Macready, W. G., 1997, No free lunch theorems for optimization, IEEE Trans. Evol. Comput. 1:67–82.
Yao, X., 1991, Evolution of connectionist networks, in: Preprints of the Int. Symp. on AI, Reasoning and Creativity (Griffith University, Queensland, Australia), T. Dartnall, ed., pp. 49–52.
Yao, X., 1993a, A review of evolutionary artificial neural networks, Int. J. Intell. Syst. 8:539–567. 28:417–425.
Yao, X., 1993b, An empirical study of genetic operators in genetic algorithms, Microprocess. Microprogram. 38:707–714.
Yao, X., 1994, The evolution of connectionist networks, in: Artificial Intelligence and Creativity, T. Dartnall, ed., Kluwer, Dordrecht, pp. 233–243.
Yao, X., 1995, Evolutionary artificial neural networks, in: Encyclopedia of Computer Science and Technology, Vol. 33, A. Kent and J. G. Williams, ed., Dekker, New York, pp. 137–170.
Yao, X., 1999, Evolving artificial neural networks, Proc. IEEE 87:1423–1447.
Yao, X. and Liu, Y., 1997, A new evolutionary system for evolving artificial neural networks, IEEE Trans. Neural Networks 8:694–713.
Yao, X. and Liu, Y., 1998, Making use of population information in evolutionary artificial neural networks, IEEE Trans. Syst., Man Cybernet. B 28:417–425.
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Yao, X., Liu, Y. (2005). Machine Learning. In: Burke, E.K., Kendall, G. (eds) Search Methodologies. Springer, Boston, MA. https://doi.org/10.1007/0-387-28356-0_12
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