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Multi-Objective Machine Learning (Studies in Computational Intelligence) (Studies in Computational Intelligence)March 2006
Publisher:
  • Springer-Verlag
  • Berlin, Heidelberg
ISBN:978-3-540-30676-4
Published:01 March 2006
Abstract

No abstract available.

Cited By

  1. Lal N, Kumar S and Chaurasiya V (2019). An adaptive neuro-fuzzy inference system-based caching scheme for content-centric networking, Soft Computing - A Fusion of Foundations, Methodologies and Applications, 23:12, (4459-4470), Online publication date: 1-Jun-2019.
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  3. Gorzałczany M and Rudziński F (2017). Interpretable and accurate medical data classification - a multi-objective genetic-fuzzy optimization approach, Expert Systems with Applications: An International Journal, 71:C, (26-39), Online publication date: 1-Apr-2017.
  4. Jimnez F, Snchez G, Garca J, Sciavicco G and Miralles L (2017). Multi-objective evolutionary feature selection for online sales forecasting, Neurocomputing, 234:C, (75-92), Online publication date: 19-Apr-2017.
  5. Albukhanajer W, Jin Y and Briffa J (2017). Classifier ensembles for image identification using multi-objective Pareto features, Neurocomputing, 238:C, (316-327), Online publication date: 17-May-2017.
  6. Basto-Fernandes V, Yevseyeva I, Méndez J, Zhao J, Fdez-Riverola F and T.M. Emmerich M (2016). A spam filtering multi-objective optimization study covering parsimony maximization and three-way classification, Applied Soft Computing, 48:C, (111-123), Online publication date: 1-Nov-2016.
  7. Jia B, Yu B, Wu Q, Yang X, Wei C, Law R and Fu S (2016). Hybrid local diffusion maps and improved cuckoo search algorithm for multiclass dataset analysis, Neurocomputing, 189:C, (106-116), Online publication date: 12-May-2016.
  8. Karami A and Guerrero-Zapata M (2015). An ANFIS-based cache replacement method for mitigating cache pollution attacks in Named Data Networking, Computer Networks: The International Journal of Computer and Telecommunications Networking, 80:C, (51-65), Online publication date: 7-Apr-2015.
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    Freitas A (2014). Comprehensible classification models, ACM SIGKDD Explorations Newsletter, 15:1, (1-10), Online publication date: 17-Mar-2014.
  10. Shelokar P, Quirin A and Cordón Ó (2013). MOSubdue, Knowledge and Information Systems, 34:1, (75-108), Online publication date: 1-Jan-2013.
  11. Wang Z and Liu D (2013). Data-based stability analysis of a class of nonlinear discrete-time systems, Information Sciences: an International Journal, 235, (36-44), Online publication date: 1-Jun-2013.
  12. Jin Y and Sendhoff B (2009). A systems approach to evolutionary multiobjective structural optimization and beyond, IEEE Computational Intelligence Magazine, 4:3, (62-76), Online publication date: 1-Aug-2009.
  13. Abbass H, Alam S and Bender A (2009). Application notes, IEEE Computational Intelligence Magazine, 4:3, (29-36), Online publication date: 1-Aug-2009.
  14. Herrero J, Berlanga A and López J (2009). Effective evolutionary algorithms for many-specifications attainment, IEEE Transactions on Evolutionary Computation, 13:1, (151-168), Online publication date: 1-Feb-2009.
  15. Alcalá R, Ducange P, Herrera F, Lazzerini B and Marcelloni F (2009). A multiobjective evolutionary approach to concurrently learn rule and data bases of linguistic fuzzy-rule-based systems, IEEE Transactions on Fuzzy Systems, 17:5, (1106-1122), Online publication date: 1-Oct-2009.
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  18. Ishida C, De Carvalho A, Pozo A, Goldbarg E and Goldbarg M Exploring multi-objective PSO and GRASP-PR for rule induction Proceedings of the 8th European conference on Evolutionary computation in combinatorial optimization, (73-84)
  19. Cuéllar M, Delgado M and Pegalajar M Topology optimization and training of recurrent neural networks with Pareto-based multi-objective algorithms Proceedings of the 9th international work conference on Artificial neural networks, (359-366)
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