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2014 | OriginalPaper | Chapter

17. Machine Learning

Authors : Xin Yao, Yong Liu

Published in: Search Methodologies

Publisher: Springer US

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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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Literature
Battiti R, Colla AM (1994) Democracy in neural nets: voting schemes for classification. Neural Netw 7:691–707CrossRef
Breiman L (1996) Bagging predictors. Mach Learn 24:123–140
Breiman L, Friedman J, Olshen RA, Stone PJ (1984) Classification and regression trees. Wadsworth, Belmont
Chandra A, Yao X (2006) Ensemble learning using multi-objective evolutionary algorithms. J Math Model Algorithms 5:417–445CrossRef
Chen H, Yao X (2009) Regularized negative correlation learning for neural network ensembles. IEEE Trans Neural Netw 20:1962–1979CrossRef
Chen H, Yao X (2010) Multiobjective neural network ensembles based on regularized negative correlation Learning. IEEE Trans Knowl Data Eng 22:1738–1751CrossRef
Cheng J, Greiner R, Kelly J, Bell DA, Liu W (2002) Learning Bayesian networks from data: an information-theory based approach. Artif Intell 137:43–90CrossRef
Clemen RT, Winkler RL (1985) Limits for the precision and value of information from dependent sources. Oper Res 33:427–442CrossRef
Dietterich TG (1997) Machine-learning research: four current directions. AI Mag 18:97–136
Domingos P, Pazzani M (1996) Beyond indpendence: conditions for the optimality of the simple Bayesian classifier. In: Saitta L (ed) Proceedings of the 13th international conference on machine learning, Bari. Morgan Kaufmann, San Mateo, pp 105–112
Drucker H, Schapire R, Simard P (1993) Improving performance in neural networks using a boosting algorithm. In: Hanson SJ et al (eds) Advances in neural information processing systems 5. Morgan Kaufmann, San Mateo, pp 42–49
Drucker H, Cortes C, Jackel LD, LeCun Y, Vapnik V (1994) Boosting and other ensemble methods. Neural Comput 6:1289–1301CrossRef
Efron B, Tibshirani RJ (1993) An introduction to the bootstrap. Chapman and Hall, London
Elkan C (1997) Boosting and naive Bayesian learning. Technical report, Department of Computer Science and Engineering, University of California
Feigenbaum EA (1961) The simulation of verbal learning behavior. In: Proceedings of the western joint computer conference, Los Angeles, pp 121–131
Fogel DB (1995) Evolutionary computation: towards a new philosophy of machine intelligence. IEEE, New York
Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution. Wiley, New York
Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In: Proceedings of the 13th international conference on machine learning, Bari. Morgan Kaufmann, San Mateo, pp 148–156
Geman S, Bienenstock E, Doursat R (1992) Neural networks and the bias/variance dilemma. Neural Comput 4:1–58CrossRef
Hansen LK, Salamon P (1990) Neural network ensembles. IEEE Trans Pattern Anal Mach Intell 12:993–1001CrossRef
Hebb DO (1949) The organization of behavior: a neurophysiological theory. Wiley, New York
Heckerman D (1998) A tutorial on learning with Bayesian networks. In: Jordan MI (ed) Learning in graphical models. Kluwer, Dordrecht
Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Nat Acad Sci USA 79:2554–2558CrossRef
Hopfield JJ, Tank DW (1985) Neural computation of decisions in optimization problems. Biol Cybern 52:141–152
Hunt EB, Marin J, Stone PT (1966) Experiments in induction. Academic, New York
Islam MM, Yao X, Murase K (2003) A constructive algorithm for training cooperative neural network ensembles. IEEE Trans Neural Netw 14:820–834CrossRef
Jacobs RA (1997) Bias/variance analyses of mixture-of-experts architectures. Neural Comput 9:369–383CrossRef
Jacobs RA, Jordan MI, Barto AG (1991a) Task decomposition through competition in a modular connectionist architecture: the what and where vision task. Cogn Sci 15:219–250CrossRef
Jacobs RA, Jordan MI, Nowlan SJ, Hinton GE (1991b) Adaptive mixtures of local experts. Neural Comput 3:79–87CrossRef
Jordan MI, Jacobs RA (1994) Hierarchical mixtures-of-experts and the EM algorithm. Neural Comput 6:181–214CrossRef
Kaelbling LP, Littman ML, Moore AW (1996) Reinforcement learning: a survey. J Artif Intell Res 4:237–285
Kim J, Ahn J, Cho S (1995) Ensemble competitive learning neural networks with reduced input dimensions. Int J Neural Syst 6:133–142CrossRef
Kodratoff Y, Michalski RS (eds) (1990) Machine learning—an artificial intelligence approach 3. Morgan Kaufmann, San Mateo
Krogh A, Vedelsby J (1995) Neural network ensembles, cross validation, and active learning. In: Tesauro G et al (eds) Advances in neural information processing systems 7. MIT, Cambridge, pp 231–238
Langley P (1996) Elements of machine learning. Morgan Kaufmann, San Francisco
Langley P, Simon H (1995) Applications of machine learning and rule induction. Commun ACM 38:54–64CrossRef
Lavrač N, Džeroski S (1994) Inductive logic programming: techniques and applications. Ellis Horwood, Chichester
Liu Y, Yao X (1998a) Negatively correlated neural networks can produce best ensembles. Aust J Intell Inf Process Syst 4:176–185
Liu Y, Yao X (1998b) A cooperative ensemble learning system. In: Proceedings of the IJCNN 1998, Anchorage. IEEE, Piscataway, pp 2202–2207
Liu Y, Yao X (1999a) Simultaneous training of negatively correlated neural networks in an ensemble. IEEE Trans Syst Man Cybern B 29:716–725CrossRef
Liu Y, Yao X (1999b) Ensemble learning via negative correlation. Neural Netw 12:1399–1404CrossRef
Liu Y, Yao X, Higuchi T (2000) Evolutionary ensembles with negative correlation learning. IEEE Trans Evol Comput 4:380–387CrossRef
Liu Y, Yao X, Higuchi T (2001) Ensemble learning by minimizing mutual information. In: Proceedings of the 2nd international conference on software engineer, artificial intelligence, networking and parallel/distributed computing, Nagoya. International association for computer and information science, pp 457–462
Liu Y, Yao X, Zhao Q, Higuchi T (2002) An experimental comparison of neural network ensemble learning methods on decision boundaries. In: Proceedings of the IJCNN 2002, Honolulu. IEEE, Piscataway, pp 221–226
McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–137CrossRef
Meir R (1995) Bias, variance, and the combination of least squares estimators. In: Tesauro G, Touretzky DS, Leen TK (eds) Advances in neural information processing systems 7. MIT, Cambridge, pp 295–302
Michalski RS, Carbonell JG, Mitchell TM (eds) (1983) Machine learning—an artificial intelligence approach 1. Morgan Kaufmann, San Mateo
Michalski RS, Carbonell JG, Mitchell TM (eds) (1986) Machine learning—an artificial intelligence approach 2. Morgan Kaufmann, San Mateo
Michie D, Spiegelhalter DJ, Taylor CC (1994) Machine learning, neural and statistical classification. Ellis Horwood, London
Minku LL, White A, Yao X (2010) The impact of diversity on on-line ensemble learning in the presence of concept drift. IEEE Trans Knowl Data Eng 22:730–742CrossRef
Minsky ML, Papert S (1969) Perceptrons: an introduction to computational geometry. MIT, Cambridge
Mitchell TM (1997) Machine learning. McGraw-Hill, New York
Muggleton SH (1995) Inverse entailment and progol. New Gener Comput 13:245–286CrossRef
Muggleton SH, Buntine W (1988) Machine invention of first-order predicates by inverting resolution. In: Proceedings of the 5th international conference on machine learning, Ann Arbor. Morgan Kaufmann, San Mateo, pp 339–352
Opitz DW, Shavlik JW (1996) Actively searching for an effective neural network ensemble. Connect Sci 8:337–353CrossRef
Perrone MP, Cooper LN (1993) When networks disagree: ensemble methods for hybrid neural networks. In: Mammone RJ (ed) Neural networks for speech and image processing. Chapman and Hall, London
Quinlan JR (1986) Introduction to decision tree. Mach Learn 1:81–106
Quinlan JR (1990) Learning logical definitions from relations. Mach Learn 5:239–266
Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann, San Mateo
Raviv Y, Intrator N (1996) Bootstrapping with noise: an effective regularization technique. Connect Sci 8:355–372CrossRef
Rogova G (1994) Combining the results of several neural networks classifiers. Neural Netw 7:777–781CrossRef
Rosen BE (1996) Ensemble learning using decorrelated neural networks. Connect Sci 8:373–383CrossRef
Rosenblatt F (1962) Principles of neurodynamics: perceptrons and the theory of brain mechanisms. Spartan, Chicago
Rumelhart DE, McClelland JL (ed) (1986) Parallel distributed processing: explorations in the microstructures of cognition. MIT, Cambridge
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Rumelhart DE, McClelland JL (eds) Parallel distributed processing: explorations in the microstructures of cognition I. MIT, Cambridge, pp 318–362
Russell S, Norvig P (2002) Artificial intelligence: a modern approach. Prentice-Hall, Englewood Cliffs
Samuel AL (1959) Some studies in machine learning using the game of checkers. IBM J Res Dev 3:210–229CrossRef
Sarkar D (1996) Randomness in generalization ability: a source to improve it. IEEE Trans Neural Netw 7:676–685CrossRef
Schapire RE (1990) The strength of weak learnability. Mach Learn 5:197–227
Schapire RE (1999) Theoretical views of boosting and applications. In: Proceedings of the 10th international conference on algorithmic learning theory, Tokyo. Springer, Berlin, pp 13–25
Schwefel HP (1981) Numerical optimization of computer models. Wiley, Chichester
Schwefel HP (1995) Evolution and optimum seeking. Wiley, New York
Shavlik J, Dietterich T (eds) (1990) Readings in machine learning. Morgan Kaufmann, San Mateo
Stone M (1974) Cross-validatory choice and assessment of statistical predictions. J R Stat Soc 36:111–147
Sutton RS, Barto AG (1998) Reinforcement learning: an introduction. MIT, Cambridge
Tang K, Lin M, Minku FL, Yao X (2009) Selective negative correlation learning approach to incremental learning. Neurocomputing 72:2796–2805CrossRef
Vapnik VN (1995) The nature of statistical learning theory. Springer, New YorkCrossRef
Wang S, Yao X (2009a) Theoretical study of the relationship between diversity and single-class measures for class imbalance learning. In: Proceedings of the IEEE international conference on data mining workshops, Miami. IEEE Computer Society, Washington, DC, pp 76–81
Wang S, Yao X (2009b) Diversity exploration and negative correlation learning on imbalanced data sets. In: Proceedings of the IJCNN 2009, Atlanta, pp 3259–3266
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67–82CrossRef
Yao X (1991) Evolution of connectionist networks. In: Dartnall T (ed) Preprints of the international symposium on AI, reasoning and creativity, Griffith University, Queensland, pp 49–52
Yao X (1993a) A review of evolutionary artificial neural networks. Int J Intell Syst 8:539–567CrossRef
Yao X (1993b) An empirical study of genetic operators in genetic algorithms. Microprocess Microprogr 38:707–714CrossRef
Yao X (1994) The evolution of connectionist networks. In: Dartnall T (ed) Artificial intelligence and creativity. Kluwer, Dordrecht, pp 233–243CrossRef
Yao X (1995) Evolutionary artificial neural networks. In: Kent A, Williams JG (eds) Encyclopedia of computer science and technology 33. Dekker, New York, pp 137–170
Yao X, Liu Y (1997) A new evolutionary system for evolving artificial neural networks. IEEE Trans Neural Netw 8:694–713CrossRef
Yao X, Liu Y (1998) Making use of population information in evolutionary artificial neural networks. IEEE Trans Syst Man Cybern B 28:417–425
Yao X, Liu Y, Darwen P (1996) How to make best use of evolutionary learning. In: Stocker R, Jelinek H, Durnota B (eds) Complex systems: from local interactions to global phenomena. IOS, Amsterdam, pp 229–242
Metadata
Title
Machine Learning
Authors
Xin Yao
Yong Liu
Copyright Year
2014
Publisher
Springer US
Book
Search Methodologies

Print ISBN: 978-1-4614-6939-1

Electronic ISBN: 978-1-4614-6940-7

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-1-4614-6940-7_17