Geodesic
An investigation of information intelligence retrieval model in local searching systems
Journal of computational and engineering mathematics, Tome 5 (2018) no. 2, pp. 77-82.

Voir la notice de l'article provenant de la source Math-Net.Ru

The process of building and learning neural networks requires considerable computational effort. Fortunately, nowadays there are many various systems with high computing power. Development of systems, algorithms, methods and their applications in machine learning has become a promising direction in science. The work is devoted to the investigation of information retrieval problems and the main problems associated with this task, constructing an overfit-safe neural network model for improving the local searching system quality on 49 % using machine learning algorithms.
Keywords: information retrieval, searching system, machine learning, neural networks, mathematical modeling, relevance increasing. 
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T. Yu. Olenchikova; D. L. Maslennikov; A. D. Marchenko. An investigation of information intelligence retrieval model in local searching systems. Journal of computational and engineering mathematics, Tome 5 (2018) no. 2, pp. 77-82. http://geodesic.mathdoc.fr/item/JCEM_2018_5_2_a6/

[1] C. D. Manning, P. Raghavan, H. Schütze, Introduction to Information Retrieval, Cambridge University Press, Cambridge, 2008 | DOI | Zbl

[2] T. Rashid, Make Your Own Neural Network, CreateSpace Independent Publishing Platform, 2016

[3] T. Segaran, Programming Collective Intelligence Building Smart Web 2.0 Applications, O'Reilly Media, 2008

[4] D. J Kingma, J. L. Ba, Adam: A Method for Stochastic Optimization, arXiv: 1412.6980v9

[5] A. Krogh, J. A. Hertz, “A Simple Weight Decay Can Improve Generalization”, Proceedings of the 4th International Conference on Neural Information Processing Systems, Morgan Kaufmann, San Francisco, 1991, 950–957

[6] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting”, The Journal of Machine Learning Research, 15:1 (2015), 1929–1958 | MR