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Machine Learning: Techniques and Application

Rafi Ahmad Khan

Abstract


Machine Learning (ML) is the ability of the machine to learn from the previous experience or history and perform better at a given task, as the future mimics the past. It is considered as a subfield of Artificial Intelligence (AI) and it is concerned with the development of techniques and methods which enable the computer to learn. In simple terms, it is considered as the science of development of algorithms which enable the machine to learn and perform tasks and activities. ML is a proven to have significant impact on both industry and research and there are numerous successful applications of ML. This paper discusses the definitions ML, various techniques of ML and highlights its various applications in research and business areas.


Keywords


Machine Learning, Neural Networks, Self-Organizing Maps (SOM), Genetic Algorithm.

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References


E. Turban, Decision Support System and Business Intelligence, Pearson Education, 2012.

T. M. Mitchell, "The need for biases in learning generalizations," CBM-TR 5-110, Rutgers University, New Jersey, USA, 1980.

I. Bruha, "Pre- and Post-processing in Machine Learning and Data Mining," in Machine Learning and its Applications - ACAI’99, 2001.

P. Langley and H. A. Simon, "Applications of machine learning and rule induction," Communications of the ACM, vol. 38, no. 11, pp. 54-64, Nov. 1995.

J. R. Quinlan, "Discovering rules by induction from large collections of examples," in Expert systems in the micro induction electronic age, Edinburg, Edinburgh University Press, 1979, p. 168–201.

D. E. Rumelhart, G. E. Hinton and R. J. Williams, "Learning internal representations by error propagation," in Parallel Distributed Processing: Explorations in the microstructure of cognition, MIT Press, 1986, pp. 318-361.

D. E. Goldberg, Genetic algorithms in search, optimization, and machine learning, MA: Addison-Wesley, 1989.

P. J. M. Van-Laarhoven and E. H. L. Aarts, Simulated annealing: Theory and applications, Reidel Publishing Company, 1988.

R. K. Belew, "Adaptive information retrieval," in Twelfth Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval, New York, 1989.

H. Chen and K. J. Lynch, "Automatic construction of networks of concepts characterizing document databases," IEEE Transactions on Systems, Man and Cybernetics, vol. 22, no. 5, p. 885–902, 1992.

H. Chen, K. J. Lynch, K. Basu and D. T. Ng, "Generating, integrating, and activating thesauri for concept-based document retrieval," IEEE EXPERT, Special Series on Artificial Intelligence in Text-based Information Systems, vol. 8, no. 2, p. 25–34, 1993.

M. Gordon, "Probabilistic and genetic algorithms for document retrieval," Communications of the ACM, vol. 31, no. 10, p. 1208–1218, 1988.

K. L. Kwok, "A neural network for probabilistic information retrieval," in Twelfth Annual International ACM/SIGIR Conference on Research and Development in Information Retrieva, New York, 1989.

F. Rosenblatt, "The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain," Psychological Review, pp. 386-408, 1957.

M. Minsky and S. A. Papert, An Introduction to Computational Geometry, Cambridge: MIT Press, 1969.

W. Royce, "Managing the Development of Large Software Systems," Proceedings of IEEE WESCON, pp. 1-9, 1970.

T. R. Mitchell, "Motivation: New directions for theory and research," Academy of Management Review, pp. 80-88, 1982.

J. R. Quinlan, "Learning efficient classification procedures and their application to chess end games," Machine Learning: An Artificial Intelligence Approach, 1983.

D. E. Rumelhart and J. L. McClelland, "Parallel Distributed Processing: Explorations in the Microstructure of Cognition," MIT Press, 1986.

A. T. Oladipupo, "Types of Machine Learning Algorithms," in New Advances in Machine Learning, InTech, 2010.

P. Tan, M. Steinbach and V. Kumar, Introduction to data mining, Boston: Pearson Addison Wesley, 2005.

P. Chan, W. Fan, A. Prodromidis and S. Stolfo, "Distributed data mining in credit card fraud detection. Intelligent Systems and Their Applications," IEEE, pp. 67-74, 1999.

H. L. Jensen, "Using neural networks for credit scoring, Managerial Finance," Managerial Finance, vol. 18, no. 6, pp. 15 - 26, 1992.

H. Simon, Neural Networks, A Comprehensive Foundation, New York: Prentice Hall, 1994.

P. A. Engelbrecht, "Computational Intelligence - An Introduction," South Africa, John Wiley , 2007.

R. Grothmann, "Multi-Agent Market Modeling Based On Neural Networks," prentice hall, 2004.

F. Laurene, Fundamentals of Neural Networks Architectures, Algorithms,and Applications, Prentice-Hal, 1994.

C. Stergiou and D. Sigsnos, "Neural Networks," Imperial College of Science and Technology, London, 1996.

B. E. Boser, I. M. Guyon and V. N. Vapnik, "A training algorithm for optimal margin classifier," in 5th ACM Workshop on Computational Learning Theory, 1992.

N. Cristianini and J. Shawe-Taylor, An introduction to support vector machines, England: Cambridge University Press, 2000.

S. R. Gunn, "Support vector machines for classification and regression," University of Southampton, 1998.

M. A. Hearst, S. T. Dumais, E. Osman, J. Platt and B. Scholkopf, "Support vector machines," IEEE Intelligent System, vol. 13, no. 4, p. 18–28, 1998.

V. Vapnik, Statistical learning theory, New York: Springer, 1998.

J. R. Quinlan, C4.5: Programs for Machine Learning, Morgan-Kaufmann, 1993.

P. Clark and T. Niblett, "The CN2 induction algorithm," Machine Learning, p. 261–283, 1989.

R. S. Michalski, "On the quasi-minimal solution of the general covering problem," in First International Symposium on Information Processing, Bled, Yugoslavia, 1969.

W. W. Cohen, "Fast effective rule induction. In Proceedings of the , 1995. M.H. Dunham. Data Mining. Prentice Hall.," in Twelfth International Conference on Machine Learning ICML95, Tahoe City, USA, 1995.

Z. Pawlak, "Rough sets," Int. J. Comp. Sci., vol. 11, pp. 341-356, 1982.

A. Skowron, "The rough sets theory and evidence theory," Fundamenta Informaticae, pp. 245-262, 1990.

Z. Pawlak, Rough Sets. Theoretical Aspects of Reasoning about Data, Dordrecht: Kluwer Academic Publishers, 1991.

S. Greco, B. Matarazzo and R. Slowinski, "Rough Sets Theory for Multicriteria Decision Analysis," European Journal of Operational Research, vol. 129, no. 1, p. 1–47, 2001.

M. Obersteiner and S. Wilk, Determinants of Long-term Economic Development: An Empirical Cross-country Study Involving Rough Sets Theory and Rule Induction, Vienna: Insititute for Advanced Studies, 1999.

T. Kohonen, "Self-organized formation of topologically correct feature maps," Biological Cybernetics, pp. 59-69, 1982.

T. Kohonen, Self-Organizing Maps, Springer, 2001.

T. Honkela, T. Leinonen, K. Lonka and A. Raike, "Self-Organizing Maps and Constructive Learning," in ICEUT, Beijing, 2000.

S. Grossberg, "Adaptive pattern classification and universal recoding, I: Parallel development and coding of neural feature detectors & II: Feedback, expectation, olfaction, and illusions," Biological Cybernetics, pp. 121-134 & 187-202, 1976.

J. Singh and N. Sharma, "Design a Neural Network Based on Hebbian Learning and ART," IJCST, vol. 2, no. 4, pp. 157-160, Oct-Dec, 2011.

G. A. Carpenter and S. Grossberg, "A massively parallel architecture for a self-organizing neural pattern recognition machine," Computer Vision, Graphics, and Image Processing, vol. 37, pp. 54-115, 1987.

F. H. T. Vieira and L. L. Lee, "A Neural Architecture Based on the Adaptive Resonant Theory and Recurrent Neural Networks," International Journal of Computer Science & Applications, pp. 45-56, 2007.

M. H. Dunham, Data Mining, Prentice Hall, 2003.

R. S. Sutton and A. G. Barto, Reinforcement Learning:An Introduction, Massachusetts : MIT Press , 1998.

A. Barto, R. Sutton and C. W. Anderson, "Neuronlike elements that can solve difficult learning control problems," IEEE Transactions on Systems, Man, and Cybernetics, pp. 835-846, 1983.

L. P. Kaelbling, M. L. Littman and A. W. Moore, "Reinforcement Learning: A Survey," Journal of Artificial Intelligence Research, pp. 237-285, 1996.

C. J. C. H. Watkins, "Learning from delayed rewards," Cambridge University, Cambridge, 1989.

E. Even-Dar and Y. Mansour, "Learning Rates for Q-learning," Journal of Machine Learning Research, pp. 1-25, 2003.

G. Rummery and M. Niranjan, "On-line Q-learning using Connectionist systems, technical report no.166," University of Cambridge, Cambridge, 1994.

R. S. Sutton, "Generalization in reinforcement learning:Successful examples using sparse coarse coding," Advances in Neural Information Processing Systems, vol. 8, p. 1038–1045, 1996.

C. H. C. Ribeiro, "A Tutorial on Reinforcement Learning Techniques," in Proceedings of International Conference on Neural Networks, Washington, DC, 2000.

Y. Singh, P. K. Bhatia and O. Sangwan, "A Review of Studies on Machine Learning Techniques," International Journal of Computer Science and Security, pp. 70-84, 2007.

E. Falkenauer, Genetic Algorithms and Grouping Problems, John Wiley & Sons, 1998.

F. P. T. Dellaert and A. Waibel, "Recognizing emotion in speech," in Fourth International Conference on Spoken Language Processing, 1996.

V. A. Petrushin, "Emotion in speech: recognition and application to call centers," in Proceedings of Artificial Neural Networks in Engineering, 1999.

J. Nicholson, K. Takahashi and R. Nakatsu, "Emotion recognition in speech using neural networks," in 6th International Conference on Neural Information Processing, 1999.

F. Yu, E. Chang, Y. Q. Xu and H. Y. Shum, "Emotion detection from speech to enrich multimedia content," in Proceedings of Second IEEE Pacific-Rim Conference on Multimedia, Beijing, China, 2001.

C. Lee and S. Narayanan, "Toward detecting emotions in spoken dialogs," IEEE Transactions on Speech and Audio Processing, vol. 13, no. 2, pp. 293-303, 2005.

N. Chauhan, "Facial Recognition System," 23 11 2011. [Online]. Available: http://www.authorstream.com/Presentation/nayan6460-1257599-facial-recognition-system/.

T. Mitchell, "The Discipline of Machine Learning," Carnegie Mellon University, Pittsburgh, USA, 2006.

S. Castro, "PC Games Guide," 28 12 2009. [Online]. Available: http://gametrap.net/?p=46.


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