An Information Reinstatement Dealing with Machine Learning
AbstractInformation retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. The process of machine learning is similar to that of data mining. Both systems search through data to look for patterns. Machine learning programs detect patterns in data and adjust program actions accordingly. In this paper, we are exploring the use of machine learning techniques for information retrieval and we are using machine learning algorithms that can benefit from limited training data in order to identify a ranker likely to achieve high retrieval performance over unseen documents and queries. This problem presents novel challenges compared to traditional learning tasks, such as regression or classification. We are investigating the discriminative learning of ad-hoc retrieval models. For that purpose, we propose different models based on kernel machines or neural networks adapted to different retrieval contexts. The proposed approaches rely on different online learning algorithms that allow efficient learning over large collection and finally approaches rely on discriminative learning and enjoy efficient training procedures, which yields effective and scalable models.
(1) Frakes, William B. (1992). Information Retrieval Data Structures & Algorithms. Prentice-Hall, Inc. ISBN 0-13-463837-9.
(2) N. J. Belkin and W. B. Croft. Information filtering and information retrieval: Two sides of the same coin? Communications of the ACM, 35(12):29–38, 1992.
(3) Singhal, Amit (2001). "Modern Information Retrieval: A Brief Overview". Bulletin of the IEEE Computer Society Technical Committee on Data Engineering 24 (4): 35–43.
(4) STANFILL, C. (1990a). Information Retrieval Using Parallel Signature Files. IEEE Data Engineering Bulletin, 13 (1), 33-40.
(5) C. M. Bishop, “Pattern Recognition and Machine Learning (Information Science and Statistics),” Aug. 2006.
(6) Dr. Yusuf Perwej, (2015), “An Evaluation of Deep Learning Miniature Concerning in Soft Computing” , International Journal of Advanced Research in Computer and Communication Engineering
(IJARCCE), Vol 04, Issue 02, pp 10 – 16, 28, ISSN (Print) 2319-5940, ISSN (Online) 2278-1021, with Impact Factor = 2.117 DOI : 10.17148/IJARCCE.2015.4203
(7) Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
(8) C. Ji and S. Ma, “Performance and efficiency: Recent advances supervised in learning,” Proc. IEEE, vol. 87, pp. 1519–1535, Sept. 1999.
(9) S. Zribi Boujelbene, D. Ben Ayed Mezghani, and N. Ellouze, “Support Vector Machines approaches and its application to speaker identification”, IEEE International Conference on Digital Eco-Systems and Technologies DEST-09, Turkey, pp. 662-667, Jun 2009.
(10) Belkin, N. and W. Croft, “Retrieval Techniques”, in Annual Review of Information Science and Technology, Elsevier Science publishers, New York, 1989, pages 109-145.
(11) Card, K., “Visualizing Retrieved Information: A Survey”, IEEE Computer Graphics and Applications, Vol. 16, No. 2, March 1996, pages 63-67.
(12) Chalmers, M. and P. Chitson, “Bead: Explorations in Information Retrieval”, Proceedings of SIGIR 92, Copenhagen, Denmark, June 1992, pages 330-337.
(13) Crew, B. and M. Gunzburg, “Information Storage and Retrieval”, U.S. Patent 3, 358, 270, December 12, 1967.
(14) Leek, T., Miller, D. and R Schwartz, "A Hidden Markov Model Information retrieval Ssystem", In Proceedings of the 22nd Annual ACM SIGIR Conference on Research and Development in Information Retrieval, 1999, pages214-221.
(15) J. S. Bridle. An efficient elastic-template method for detecting given words in running speech. In British Acoustic Society Meeting, pages 1–4, London, UK, April 1973.
(16) L. R. Bahl, P. F. Brown, P. de Souza, and R. L. Mercer. ,“Maximum mutual information estimation of hidden markov model parameters for speech recognition” , In International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pages 49–52, Tokyo, Japan, April 1986.
(17) J. G. Wilpon, L. R. Rabiner, C. H. Lee, and E. R. Goldman. ,“Automatic recognition of keywords in unconstrained speech using hidden markov models”, IEEE Transactions on Acoustics, Speech and Signal Processing (TASSP), 38 (11):1870–1878, 1990.
(18) J. A. Bilmes. A gentle tutorial of the EM algorithm and its application to parameter estimation for gaussian mixture and hidden markov models. Technical Report TR-97-021, International Computer Science Institute, Berkeley, CA, USA, 1998.
(19) M. Weintraub. LVCSR log-likelihood ratio scoring for keyword spotting. In International Conference on Acoustics, Speech, and Signal Processing (ICASSP), volume 1, pages 297–300, Detroit, MI, USA, May 1995.
(20) E. D. Sandness and I. Lee Hetherington. Keyword-based discriminative training of acoustic models. In International Conference on Spoken Language Processing (ICSLP), volume 3, pages 135–138, Beijing, China, October 2000.
(21) Y. Benayed, D. Fohr, J. P. Haton, and G. Chollet. Confidence measures for keyword spotting using support vector machines. In International Conference on Acoustics, Speech, and Signal Processing (ICASSP), volume 1, pages 588–591, Hong Kong, China, April 2003.
(22) R. A. Sukkar, A. R. Seltur, M. G. Rahim, and C. H. Lee. Utterance verification of keyword strings using word-based minimum verification error training. In International Conference on Acoustics, Speech, and Signal Processing (ICASSP), volume 1, pages 518–521, Atlanta, GA,
USA, May 1996.
(23) Dr. Yusuf Perwej, “The Bidirectional Long-Short-Term Memory Neural Network based Word Retrieval for Arabic Documents” Transactions on Machine Learning and Artificial Intelligence (TMLAI) which is published by Society for Science and Education, Manchester, United Kingdom (UK), Volume 03, No.01, Pages 16 – 27, 02 February 2015, ISSN 2054 - 7390, DOI : 10.14738/tmlai.31.863
(24) Forsyth, R. and R. Rada, "Adding an Edge", in Machine Learning: application in expert systems and information retrieval, Ellis Horwood Ltd., 1986, pages 198-212.
(25) S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman. Indexing by latent semantic analysis. Journal of the American Society of Information Science, 6(41):391–407, 1990.
(26) T. Hofmann. Unsupervised learning by probabilistic latent semantic analysis. Machine Learning, 42(1):177–196, 2001.
(27) C. Cortes and M. Mohri. Confidence intervals for the area under the roc curve. In Advances in Neural Information Processing Systems (NIPS), Vancouver, Canada, December 2004.
(28) Reynolds, D.A., Rose, R.C.: Robust, “Text-Independent Speaker Identification using Gaussian Mixture Speaker Models”, IEEE Transactions on Acoustics, Speech, and Signal Processing 3(1) (1995