Citations
Clasificador bayesiano de dos clases para seleccionar la mejor regla de prioridad en un problema Job Shop: Open Shop
Clasificador bayesiano de dos clases para seleccionar la mejor regla de prioridad en un problema Job Shop: Open Shop
Open | Download
Issue
Section
Articles
How to Cite
Clasificador bayesiano de dos clases para seleccionar la mejor regla de prioridad en un problema Job Shop: Open Shop. (2019). Revista EIA, 16(31), 57-64. https://doi.org/10.24050/reia.v16i31.867
Dimensions
license
Copyright statement
The authors exclusively assign to the Universidad EIA, with the power to assign to third parties, all the exploitation rights that derive from the works that are accepted for publication in the Revista EIA, as well as in any product derived from it and, in in particular, those of reproduction, distribution, public communication (including interactive making available) and transformation (including adaptation, modification and, where appropriate, translation), for all types of exploitation (by way of example and not limitation : in paper, electronic, online, computer or audiovisual format, as well as in any other format, even for promotional or advertising purposes and / or for the production of derivative products), for a worldwide territorial scope and for the entire duration of the rights provided for in the current published text of the Intellectual Property Law. This assignment will be made by the authors without the right to any type of remuneration or compensation.
Consequently, the author may not publish or disseminate the works that are selected for publication in the Revista EIA, neither totally nor partially, nor authorize their publication to third parties, without the prior express authorization, requested and granted in writing, from the Univeridad EIA.
Article visits 399 | PDF visits 328
Downloads
Download data is not yet available.
- Baltazar, a., aranda, J. I. & Aguilar, G. G. (2008). Bayesian classification of ripening stages of tomato fruit using acoustic impact and colorimeter sensor data. computers and electronics in agriculture, No. 60, pp. 113-121.
- Dallaire, P., Giguère, P., Émond, D. & Chaib-draa, B. (2014). Autonomous tactile perception: A combined improved sensing and Bayesian nonparametric approach. Robotics and Autonomous Systems, No. 62, pp. 422-435.
- Del Sagrado, J., Sanchez, J. A., Rodriguez, F. & Berenguel, M. (2016). Bayesian networks for greenhouse temperature control. Journal of Applied Logic, http://dx.doi.org/10.1016/j.jal.2015.09.006, Article in press.
- Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Patter Classification. New York, Estados Unidos: John Wiley & Sons, Pagina 41.
- Fernandez, E. (2016). Analisis de clasificadores Bayesianos. Argentina: Laboratorio de sistemas Inteligentes, Consultado 18 de febrero de 2006, disponible en http://materias.fi.uba.ar/7550/clasificadores-bayesianos.pdf
- Hanen , B., Concha , B., Toro, C. & Larragaña, P. (2013). Predicting human immunodeficiency virus inhibitors using multi-dimensional Bayesian network classifiers. Artificial Intelligence in Medicine, No. 57, pp. 219-229.
- He, L., Liu, B., Hu, D., Wen, Y., Wan, M. & Long, J. (2015). Motor Imagery EEG Signals Analysis Based on Bayesian Network with Gaussian Distribution. Neurocomputing, http://dx.doi.org/10.1016/j.neucom.2015.05.133 (Article in press).
- Karabatak, M. (2015). A new classifier for breast cancer detection based on Naïve Bayesian. Measurement, No. 72, pp. 32-36.
- Mujalli, R. O., Lopez, G. & Garach L. (2016). Bayes classifiers for imbalanced traffic accidents data sets. Accident Analysis and Prevention, No. 88, pp. 37-51.
- Mukherjee, S. & Sharmaa, N. (2012). Intrusion Detection using Naive Bayes Classifier with Feature Reduction. Procedia Technology, No. 4, pp. 119-128.
- Roy, S., Shivakumara, P., Roy, P. P., Pal, U., Tan, C. L. & Lu, T. (2015). Bayesian classifier for multi-oriented video text recognition system. Expert Systems with Applications, No. 42, pp. 5554-5556.
- Salama, K. M. & Freitas. (2014). A. A. Classification with cluster-based Bayesian multi-nets using Ant Colony Optimization. Swarm and Evolutionary Computation, No. 18, pp. 54-70.
- Sun, L., Lin, L., Wang, Y., Gen, M. & Kawakami, H. (2015). A Bayesian Optimization-based Evolutionary Algorithm for Flexible Job Shop Scheduling. Procedia Computer Science, No. 61, pp. 521-526.
- Wiggins, M., Saad, A. & Litt, B. (2008). Vachtsevanos, G. Evolving a Bayesian classifier for ECG-based age classification in medical applications. Applied Soft Computing, No. 8, pp. 599-608.
- Xiang, C., Yong, P. C. & Meng, L. S. (2008). Design of multiple-level hybrid classifier for intrusion detection system using Bayesian clustering and decision trees. Pattern Recognition Letters, No. 29, pp. 918-924.
- Yin, W., Kissinger, J. C., Moreno, A., Galinski, M. R. & Styczynski. (2015). M. P. From genome-scale data to models of infectious disease: A Bayesian network-based strategy to drive model development. Mathematical Biosciences, No. 260, pp. 156-168.
- Zaidan, A., Ahmad, N., Karim, H. A., Larbani, M., Zaidan & B. Sali. (2014). A. On themulti-agent learning neural and Bayesian methods in skin detector and pornography classifier: An automated anti-pornography system. Neurocomputing, No. 131, pp. 397-418.
