Aprendizaje de máquinas aplicado a un sistema de chat universitario
Contenido principal de artículos
Palabras clave:
Inteligencia artificial, Maquinas de aprendizaje, Redes neuronales, Minería de datos
Resumen
El aprendizaje automático es un tipo de Inteligencia Artificial (IA) que proporciona a los equipos la capacidad de aprender sin ser programada de forma explícita. El aprendizaje automático se centra en el desarrollo de programas informáticos que pueden enseñar a sí mismos y cambiar cuando se exponen a nuevos datos. El proceso de aprendizaje de la máquina es similar a la de la minería de datos. Ambos son sistemas de búsqueda a través de datos para encontrar patrones. Sin embargo, en lugar de extraer datos para la comprensión humana-como es el caso en aplicaciones de minería de datos-aprendizaje automático utiliza esos datos para detectar patrones en los datos y ajustar las acciones del programa en consecuencia.
Citas
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[2] A. Bicchi, “Of Robots, Humans, Bodies and Intelligence: Body Languages for Human Robot Interaction”. Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction, Portland, Oregon, USA, 2015.
[3] C. Bucilu, R. Caruana & A. Niculescu-Mizil “Model compression”. Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, USA, 2006.
[4] D. Kazakov & A. R. Shahid. “Unsupervised construction of a multilingual WordNet from parallel corpora”. Proceedings of the Workshop on Natural Language Processing Methods and Corpora in Translation, Lexicography, and Language Learning, Borovets, Bulgaria, 2009.
[5] L. Zheng, S. Wang, Y. Liu, & C. H. Lee. “Information theoretic regularization for semi-supervised boosting”. Proceedings of the 15th ACM SIGKDD International Conference On Knowledge Discovery and Data Mining, Paris, France, 2009.
[6] A. Wilson, A. Fern, & P. Tadepalli, “Bayesian role discovery for multi-agent reinforcement learning”. Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, Toronto, Canada, 2010.
[7] C. K. Tyran & J. F. George, “The implementation of expert systems: a survey of successful implementations”; SIGMIS Database, vol. 24, no. 1, pp. 5-15, 1993.
[8] Q. Xu, S. J. Pan, H. H. Xue & Q. Yang. “Multitask Learning for Protein Subcellular Location Prediction”. IEEE/ACM Trans. Comput. Biol. Bioinformatics, vol. 8, no. 3, pp. 748-759, 2011.
[9] J. Lee, J. Kwon, & H. Kim. “Reducing distraction of smartwatch users with deep learning”. Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct, Florence, Italy, 2016.
[10] W. Deng, Q. Zheng, & L. Chen “Real-Time Collaborative Filtering Using Extreme Learning Machine”. Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, 2009.
[11] N. D. Lane, S. Bhattacharya, P. Georgiev, C. Forlivesi, & F. Kawsar. “Demo: Accelerated Deep Learning Inference for Embedded and Wearable Devices using DeepX”. Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services Companion, Singapore, Singapore, 2016.
[12] Y. Minami, K. Shikano, O. Yoshioka, S. Takahashi, T. Yamada, & S. Furui. “A large-vocabulary continuous speech recognition algorithm and its application to a multi-modal telephone directory assistance system”. Proceedings of the workshop on Human Language Technology, Plainsboro, NJ, 1994.
[13] M. D. Marsico, M. Nappi, & D. Riccio. “Measuring sample distortions in face recognition”. Proceedings of the 2nd ACM workshop on Multimedia in forensics, security and intelligence, Firenze, Italy, 2010.
[14] G. E. Hinton, “A better way to learn features: technical perspective”; Commun. ACM, vol. 54, no. 10, pp. 94-94, 2011.
[15] H. Barrigas, D. Barrigas, M. Barata, P. Furtado & J. Bernardino, “Overview of Facebook scalable architecture” Proceedings of the International Conference on Information Systems and Design of Communication, Lisbon, Portugal, 2014.
[16] J. Parra, L. Trujillo, & P. Melin, “Backpropagation learning with a (1+1) ES”. Proceedings of the 12th annual conference companion on Genetic and evolutionary computation, Portland, Oregon, USA, 2010.
[17] W. Choi et al “Hybrid network-on-chip architectures for accelerating deep learning kernels on heterogeneous manycore platforms”. Proceedings of the International Conference on Compilers, Architectures and Synthesis for Embedded Systems, Pittsburgh, Pennsylvania, 2016.
[18] J. Goncalves, V. Kostakos, & J. Venkatanathan, “Narrowcasting in social media: effects and perceptions”. Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Niagara, Ontario, Canada, 2013.
[19] Z. Zhang, F. Masseglia, R. Jain, and A. D. Bimbo, “KDD/MDM 2006: The KDD Multimedia Data Mining workshop report,”; SIGKDD Explor. Newsl., vol. 8, no. 2, pp. 92-95, 2006.
[20] W. W. Zadrozny, S. Gallagher, W. Shalaby, & A. Avadhani, “Simulating IBM Watson in the Classroom,”. Proceedings of the 46th ACM Technical Symposium on Computer Science Education, Kansas City, Missouri, USA, 2015.
[21] G. Banavar. “Watson and the Era of Cognitive Computing”. SIGARCH Comput. Archit. News, vol. 43, no. 1, pp. 413-413, 2015.
[22] O. Temam. “The rebirth of neural networks,”. SIGARCH Comput. Archit. News, vol. 38, no. 3, pp. 349-349, 2010.
[23] B. Londoño González and P. Sánchez, "Algoritmo Novedoso Para la Detección de Tareas Repetitivas en el Teclado", Investigacion e Innovación en Ingenierias, vol. 3, no. 2, 2015. DOI: 10.17081/invinno.3.2.2031
[24] P.A. Sánchez, F. Ceballos, G. Sánchez-Torres, “Análisis del proceso productivo de una empresa de confecciones: modelación y simulación”, Ciencia e Ingeniería Neogranadina, 25(2), 2015. DOI: 10.18359/rcin.1436
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Sep 28, 2017
Publicado
Oct 2, 2017
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