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Introduction to Modeling Problems

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Artificial Organic Networks

Part of the book series: Studies in Computational Intelligence ((SCI,volume 521))

Abstract

Computational algorithms for modeling problems are widely used in real world applications, such as: predicting behaviors in systems, describing of systems, or finding patterns on unknown and uncertain data.

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References

  1. Adleman L (1994) Molecular computation of solutions to combinatorial problems. Science 266:1021–1024

    Article  Google Scholar 

  2. Alpaydin E (2004) Introduction to Machine Learning. MIT Press, United States of America

    Google Scholar 

  3. Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv 35(3):268–308

    Article  Google Scholar 

  4. Boden M (1996) Artificial Intelligence. Academic Press, United States of America

    MATH  Google Scholar 

  5. Burden R, Faires J (2005) Numerical Analysis. Cengage Learning, United States of America

    Google Scholar 

  6. Dorigo M, Gambardella LM (1997) Ant colony systems: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66

    Article  Google Scholar 

  7. Evans J, Minieka E (1992) Optimization algorithms for networks and graphs. Marcel Dekker, United States of America

    Google Scholar 

  8. Hastie T, Tibshirani R, Friedman JH (2009) The elements of statistical learning: data mining, inference, and prediction. Springer, New York

    Google Scholar 

  9. Hromkovic J (2001) Algorithms for hard problems: introduction to combinatorial optimization, randomization, approximation, and heuristics. Springer-Verlag, Germany

    Book  Google Scholar 

  10. Hung MC, Yang DL (2001) An efficient fuzzy C-means clustering algorithm. In: Proceedings of IEEE international conference on data mining. California, San Jose, pp 225–232

    Google Scholar 

  11. Irwin G, Warwick K, Hunt K (1995) Neural network applications in control. The Institution of Electrical Engineers, England

    Book  Google Scholar 

  12. Jana PK, Sinha BP (1997) Fast parallel algorithms for forecasting. ELSEVIER Comput Math Appl 34(9):39–49

    Article  MATH  Google Scholar 

  13. Kolahdouzan MR, Shahabi C (2004) Continuous K nearest neighbor queries in spatial network databases. In: Proceedings of the 2nd workshop on spatio-temporal database management. Toronto, Canada

    Google Scholar 

  14. Kramer KA, Hall LO, Goldgof DB (2009) Fast support vector machines for continuous data. IEEE Trans Syst Man Cybern 39(4):989–1001

    Article  Google Scholar 

  15. Lam AYS, Li VOK (2010) Chemical-reaction-inspired metaheuristic for optimization. IEEE Trans Evol Comput 14(3):381–399

    Article  Google Scholar 

  16. Lazinica A (ed) (2009) Particle swarm optimization. InTech

    Google Scholar 

  17. Memisevic R (2003) Unsupervised kernel regression for nonlinear dimensionality reduction. Ph.D. thesis, Universitat Bielefeld

    Google Scholar 

  18. Mitchell T (1997) Machine Learning. McGraw Hill, United States of America

    MATH  Google Scholar 

  19. Moret B, Saphiro H (1991) Algorithms from P to NP. The Benjamin/Cummings Publishing Company, United States of America

    Google Scholar 

  20. Olariu S, Zomaya A (2006) Handbook of bioinspired algorithms and applications. CRC Press, United States of America

    MATH  Google Scholar 

  21. Pazos A, Sierra A, Buceta W (2009) Advancing artificial intelligence through biological process applications. Medical Information Science Reference, United States of America

    Google Scholar 

  22. Robnik-Sikonja M (2004) Improving random forests. In: Boulicaut JF (ed) Proceedings of ECML machine learning. Springer, Berlin

    Google Scholar 

  23. Rudich S, Wigderson A (eds) (2004) Computational complexity theory. American Mathematical Society, Providence

    Google Scholar 

  24. Schoukens J, Rolain Y, Gustafsson F, Pintelon R (1998) Fast calculation of least-squares estimates for system identification. In: Proceedings of the 37th IEEE conference on decision and control, vol 3. Tampa, Florida, pp 3408–3410

    Google Scholar 

  25. Sreenivasarao V, Vidyavathi S (2010) Comparative analysis of fuzzy C-mean and modified fuzzy possibilistic C-mean algorithms in data mining. Int J Comput Sci Technol 1(1):104–106

    Google Scholar 

  26. Su J, Zhang H (2006) A fast decision tree learning algorithm. In: Proceedings of the 21st national conference on artificial intelligence, vol 1, pp 500–505

    Google Scholar 

  27. Vens C, Costa F (2011) Random forest based feature induction. In: Proceedings of IEEE 11th international conference on data mining. Vancouver, pp 744–753

    Google Scholar 

  28. Wooldridge M (2002) An introduction to multiagent systems. John Wiley and Sons, England

    Google Scholar 

  29. Wu D, Butz C (2005) On the complexity of probabilistic inference in singly connected bayesian networks. In: Proceedings of the 10th international conference on rough sets, fuzzy sets, data mining, and granular computing, vol Part I. Springer, Berlin, pp 581–590

    Google Scholar 

  30. Wurtz RP (ed) (2008) Organic computing. Springer, Berlin

    Google Scholar 

  31. Yang XS (2010) Nature-inspired metaheuristics algorithms. Luniver Press, University of Cambridge, United Kingdom

    Google Scholar 

  32. Zhao Q, Hautamaki V, Karkkainen I, Franti P (2012) Random swap EM algorithm for gaussian mixture models. ELSEVIER Pattern Recognit Lett 33:2120–2126

    Article  Google Scholar 

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Authors and Affiliations

  1. Instituto Tecnológico de Estudios Superiores de Monterrey, Col. Ejidos de Huipulco Tlalpan, Mexico, Distrito Federal, Mexico

    Hiram Ponce-Espinosa, Pedro Ponce-Cruz & Arturo Molina

Authors
  1. Hiram Ponce-Espinosa
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  2. Pedro Ponce-Cruz
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  3. Arturo Molina
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Correspondence to Hiram Ponce-Espinosa .

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Ponce-Espinosa, H., Ponce-Cruz, P., Molina, A. (2014). Introduction to Modeling Problems. In: Artificial Organic Networks. Studies in Computational Intelligence, vol 521. Springer, Cham. https://doi.org/10.1007/978-3-319-02472-1_1

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  • DOI: https://doi.org/10.1007/978-3-319-02472-1_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02471-4

  • Online ISBN: 978-3-319-02472-1

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