Universal learning machines

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5864 LNCS, Issue PART 2, 2009, Pages 206-215

  Duch, Włodzisław ^a   Maszczyk, Tomasz ^a  

^a NICOLAUS COPERNICUS UNIVERSITY   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

DATA DISTRIBUTION; DATA MODELS; LEARNING MACHINES; LEARNING METHODS; LOCAL FEATURE; METALEARNING; MULTI-RESOLUTIONS;

BOOLEAN FUNCTIONS; DATA PROCESSING; LEARNING ALGORITHMS; LEARNING SYSTEMS;

PROBABILITY DISTRIBUTIONS;

EID: 76249127573     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-10684-2_23     Document Type: Conference Paper

References (27)

1. Schölkopf, B., Smola, A.: Learning with Kernels. Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)
2. Duch, W., Maszczyk, T.: Almost random projection machine. In: Alippi, C., et al. (eds.) ICANN 2009, Part I. LNCS, vol. 5768, pp. 789-798. Springer, Heidelberg (2009)
3. Duch, W.: k-separability. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds.) ICANN 2006. LNCS, vol. 4131, pp. 188-197. Springer, Heidelberg (2006)
4. Duch, W., Grudziński, K.: Meta-learning: searching in the model space. In: Proceedings of the International Conference on Neural Information Processing, Shanghai, pp. 235-240 (2001)
5. Duch, W., Grudziński, K.: Meta-learning via search combined with parameter optimization. In: Rutkowski, L., Kacprzyk, J. (eds.) Advances in Soft Computing, pp. 13-22. Springer-Physica Verlag, New York (2002)
6. Duda, R.O., Hart, P.E., Stork, D.: Patter Classification. J. Wiley & Sons, New York (2001)
7. Duch, W., Adamczak, R., Gra̧bczewski, K.: A new methodology of extraction, optimization and application of crisp and fuzzy logical rules. IEEE Transactions on Neural Networks 12, 277-306 (2001)
8. Duch, W., Setiono, R., Zurada, J.: Computational intelligence methods for understanding of data. Proceedings of the IEEE 92, 771-805 (2004)
9. Grochowski, M., Duch, W.: Learning highly non-separable Boolean functions using Constructive Feedforward Neural Network. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D.P. (eds.) ICANN 2007. LNCS, vol. 4668, pp. 180-189. Springer, Heidelberg (2007)
10. Brazdil, P., Giraud-Carrier, C., Soares, C., Vilalta, R.: Metalearning: Applications to Data Mining. In: Cognitive Technologies. Springer, Heidelberg (2009)
11. Duch, W., Jankowski, N.: Survey of neural transfer functions. Neural Computing Surveys 2, 163-213 (1999)
12. Duch, W., Jankowski, N.: Taxonomy of neural transfer functions. In: International Joint Conference on Neural Networks, Como, Italy, vol. III, pp. 477-484. IEEE Press, Los Alamitos (2000)
13. Duch, W.: Gra̧bczewski, K.: Heterogeneous adaptive systems. In: IEEE World Congress on Computational Intelligence, pp. 524-529. IEEE Press, Honolulu (2002)
14. Duch, W.: Towards comprehensive foundations of computational intelligence. In: Duch, W., Mandziuk, J. (eds.) Challenges for Computational Intelligence, vol. 63, pp. 261-316. Springer, Heidelberg (2007)
15. Duch, W., Itert, L.: Committees of undemocratic competent models. In: Rutkowski, L., Kacprzyk, J. (eds.) Proc. of Int. Conf. on Artificial Neural Networks (ICANN), Istanbul, pp. 33-36 (2003)
16. Grabczewski, K., Jankowski, N.: Versatile and efficient meta-learning architecture: Knowledge representation and management in computational intelligence. In: IEEE Symposium on Computational Intelligence in Data Mining, pp. 51-58. IEEE Press, Los Alamitos (2007)
17. Grabczewski, K., Jankowski, N.: Meta-learning with machine generators and complexity controlled exploration. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 545-555. Springer, Heidelberg (2008)
18. Duch, W.: Filter methods. In: Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L. (eds.) Feature extraction, foundations and applications, pp. 89-118. Springer-Physica Verlag, New York (2006)
19. Grochowski, M., Duch, W.: Projection Pursuit Constructive Neural Networks Based on Quality of Projected Clusters. In: Kůrková, V., Neruda, R., Koutník, J. (eds.) ICANN 2008, Part II. LNCS, vol. 5164, pp. 754-762. Springer, Heidelberg (2008)
20. Duch, W., Adamczak, R., Diercksen, G.: Classification, association and pattern completion using neural similarity based methods. Applied Mathematics and Computer Science 10, 101-120 (2000)
21. Asuncion, A., Newman, D.: UCI machine learning repository (2007)
22. Gra̧bczewski, K., Duch, W.: The separability of split value criterion. In: Proceedings of the 5th Conf. on Neural Networks and Soft Computing, Zakopane, Poland, Polish Neural Network Society, pp. 201-208 (2000)
23. Duch, W., Jankowski, N., Gra̧bczewski, K., Naud, A., Adamczak, R.: Ghostminer data mining software. Technical report, Department of Informatics, Nicolaus Copernicus University (2000-2008), http://www.fqspl.com.pl/ghostminer/
24. Michie, D., Spiegelhalter, D.J., Taylor, C.C.: Machine learning, neural and statistical classification. Elis Horwood, London (1994)
25. Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L.: Feature extraction, foundations and applications. Springer-Physica Verlag, Heidelberg (2006)
26. Liu, H., Motoda, H.: Feature extraction, construction and selection: a data mining perspective. In: Liu, H., Motoda, H. (eds.) SECS, vol. 453. Kluwer Academic, Boston (1998) (Includes bibliographical references and index)
27. Pȩkalska, E., Duin, R.: The dissimilarity representation for pattern recognition: foundations and applications. World Scientific, Singapore (2005)