Bibliografía#
Bibliografía#
Kiyoshi Asai, Satoru Hayamizu, and Ken'ichi Handa. Prediction of protein secondary structure by the hidden markov model. Computer Applications in the Biosciences, 9:141–146, 04 1993. doi:10.1093/bioinformatics/9.2.141.
J Barrios Arce. La matriz de confusión y sus métricas. Health Big Data https://www. juanbarrios. com/la-matriz-de-confusion-y-sus-metricas, 2019.
Concha Bielza and Pedro Larrañaga. Data-driven computational neuroscience: machine learning and statistical models. Cambridge University Press, 2020.
J.S. Bowman, L.A. Amaral-Zettler, J.J. Rich, C.M. Luria, and H.W. Ducklow. Bacterial community segmentation facilitates the prediction of ecosystem function along the coast ofthe western antarctic peninsula. The ISME Journal, 2017.
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203, 2013.
B. Chopard and M. Tomassini. An Introduction to Metaheuristics for Optimization. Springer, 2018.
J. Cortazar. Memorias de Adriano. Edhasa, 2009.
M. Cottrell, M. Olteanu, F. Rossi, and N. Villa-Vialeneix. Self-organizing maps, theory and applications. Revista de Investigación Operacional, 39(1):1–22, 2018.
P. Diaconis, S. Holmes, and R. Montgomery. Dynamical bias in the coin toss. SIAM Rev., 49(2):211–235, 2007.
M. Eigen. Self organization of matter and the evolution of biological macromolecules. Naturwissenschaften, 58:465–523, 1971.
D. Floreano and C. Mattiussi. Bio-Inspired Artificial Intelligence: Theories, Methods and Technologies. MIT, 2008.
J. Fort. Som's mathematics. Neural Networks, 19(6-7):812–816, 2006.
Ross Girshick. Fast r-cnn. In Proceedings of the IEEE international conference on computer vision, 1440–1448. 2015.
D. Greenhalgh and S. Marshall. Convergence criteria for genetic algorithms. SIAM J. Comput., 30(1):269–282, 2000.
B. Hayes. First links in the markov chain. American Scientist, 2013.
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, 2961–2969. 2017.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, 770–778. 2016.
Jeff Heaton. Introduction to neural networks with Java. Heaton Research, Inc., 2008.
Jeff Heaton, Steven McElwee, James Fraley, and James Cannady. Early stabilizing feature importance for tensorflow deep neural networks. In 2017 International Joint Conference on Neural Networks (IJCNN), 4618–4624. IEEE, 2017.
Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780, 1997.
Anil K Jain, Jianchang Mao, and K Moidin Mohiuddin. Artificial neural networks: a tutorial. Computer, 29(3):31–44, 1996.
M. (Ed.) Johnsson. Applications of Self-Organizing Maps. InTech, 2012.
IT1011 Jolliffe. Generalizations and adaptations of principal component analysis. In Principal Component Analysis, pages 223–234. Springer, 1986.
H Kaiming, Z Xiangyu, R Shaoqing, and others. Deep residual learning for image recognition. resnet model. arXiv preprint arXiv:1512.03385, 2015.
Diederik P Kingma and Jimmy Ba. Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
T. Kohonen. Analysis of a simple self-organizing process. Biol. Cybern., 44:135–140, 1982.
T. Kohonen. Self-organized formation of topologically correct feature maps. Biol. Cybern., 43:59–69, 1982.
T. Kohonen. Self-Organizing Maps. Volume 30. Springer Series in Information Science, Springer, 1995.
T. Kohonen. Self-Organizing Maps. Springer-Verlag, 3rd edition edition, 2000.
O. Kramer. Genetic Algorithm Essentials. Springer International Publishing, 2017.
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks (alexnet). Advances in Neural Information Processing Systems 25 (NIPS 2012), 2012.
Anders Krogh. What are artificial neural networks? Nature biotechnology, 26(2):195–197, 2008.
M. Kubat. An Introduction to Machine Learning. Springer, 2015.
Yann LeCun, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel. Backpropagation applied to handwritten zip code recognition. Neural computation, 1(4):541–551, 1989.
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324, 1998.
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2117–2125. 2017.
Jonathan Long, Evan Shelhamer, and Trevor Darrell. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, 3431–3440. 2015.
R. Lorenzo-Redondo, S. Delgado, F. Morán, and C. López-Galíndez. Realistic three dimensional fitness landscapes generated by self organizing maps for the analysis of experimental hiv-1 evolution. PLoS ONE, 9(2):e88579, 2014.
Warren S McCulloch and Walter Pitts. A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4):115–133, 1943.
Marina Meilă. Comparing clusterings by the variation of information. Learning theory and kernel machines. Springer, 2003.
Marina Meilă. Comparing clusterings—an information based distance. Journal of multivariate analysis, 98(5):873–895, 2007.
M. Mitchell. Complexity A Guided Tour. UOP USA, 2009.
F. Morán and F. Montero. An algorithm to study the evolution and selection of auto replicative molecules. Computers and Chemistry, 8:304–307, 1984.
J.C. Nuño and F.J. Muñoz. The partial visibility curve of the feigenbaum cascade to chaos. Chaos, Solitons and Fractals, 2020.
E. Oja and S. (Eds.) Kaski. Kohonen Maps. Elsevier Science, 1999.
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, 652–660. 2017.
W.B. Rogers, T. Sinno, and J.C. Crocker. Kinetics and non-exponential binding of dna-coated colloids. Soft Matter, 9(28):6412–6417, 2013.
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, 234–241. Springer, 2015.
Frank Rosenblatt. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6):386, 1958.
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams. Learning representations by back-propagating errors. nature, 323(6088):533–536, 1986.
Andrew W Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander WR Nelson, Alex Bridgland, and others. Protein structure prediction using multiple deep neural networks in the 13th critical assessment of protein structure prediction (casp13). Proteins: Structure, Function, and Bioinformatics, 87(12):1141–1148, 2019.
Andrew W Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander WR Nelson, Alex Bridgland, and others. Improved protein structure prediction using potentials from deep learning. Nature, 577(7792):706–710, 2020.
Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik Learned-Miller. Multi-view convolutional neural networks for 3d shape recognition. In Proceedings of the IEEE international conference on computer vision, 945–953. 2015.
C Szegedy, W Liu, Y Jia, P Sermanet, S Reed, D Anguelov, D Erhan, V Vanhoucke, and A Rabinovich. Googlenet. In Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2014.
Lijing Wang, Jiangzhuo Chen, and Madhav Marathe. Tdefsi: theory-guided deep learning-based epidemic forecasting with synthetic information. ACM Transactions on Spatial Algorithms and Systems (TSAS), 6(3):1–39, 2020.
Bernard Widrow and Marcian E Hoff. Adaptive switching circuits. Technical Report, Stanford Univ Ca Stanford Electronics Labs, 1960.
Junjie Wu, Jian Chen, Hui Xiong, and Ming Xie. External validation measures for k-means clustering: a data distribution perspective. Expert Systems with Applications, 36(3):6050–6061, 2009.
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao. 3d shapenets: a deep representation for volumetric shapes. In Proceedings of the IEEE conference on computer vision and pattern recognition, 1912–1920. 2015.
M. Yourcenar. Mémoires d'Hadrien. Plon, 1951.
J. Zhang and H. Fang. Using self-organizing maps to visualize, filter and cluster multidimensional bio-omics data. In Magnus Johnsson, editor, Applications of Self-Organizing Maps, chapter 8, pages 161–179. InTech, Croatia, 2012.