Almost random projection machine

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

Volumn 5768 LNCS, Issue PART 1, 2009, Pages 789-798

  Duch, Włodzisław ^a   Maszczyk, Tomasz ^a  

^a NICOLAUS COPERNICUS UNIVERSITY   (Poland)

Author keywords

Learning; Neural networks; Random projections

Indexed keywords

FINAL DECISION; IN-NETWORK; LEARNING; RANDOM PROJECTIONS; SIMPLER ALGORITHMS;

BACKPROPAGATION;

FEEDFORWARD NEURAL NETWORKS;

EID: 70350592025     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04274-4_81     Document Type: Conference Paper

References (21)

1. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. In: Rumelhart, D.E., McCleland, J.L. (eds.) Parallel Distributed Processing: Explorations in Microstructure of Congnition. Foundations, vol. 1, pp. 318-362. MIT Press, Cambridge (1986)
2. 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)
3. O'Reilly, R., Munakata, Y.: Computational Explorations in Cognitive Neuroscience. MIT Press, Cambridge (2000)
4. Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
5. 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)
6. Hinton, G.E., Osindero, S., Teh, Y.: A fast learning algorithm for deep belief nets. Neural Computation 18, 381-414 (2006)
7. Maass, W., Natschläger, T., Markram, H.: Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation 14, 2531-2560 (2002)
8. Schölkopf, B., Smola, A.: Learning with Kernels. Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)
9. Selfridge, O.G.: Pandemonium: A paradigm for learning. In: Blake, D.V., Uttley, A.M. (eds.) Proceedings of the Symposium on Mechanisation of Thought Processes, pp. 511-529. HM Stationery Office, London (1959)
10. Haykin, S.: Neural Networks - A Comprehensive Foundation. Maxwell MacMillian Int., New York (1994)
11. Duch, W.: Uncertainty of data, fuzzy membership functions, and multi-layer perceptrons. IEEE Transactions on Neural Networks 16, 10-23 (2005)
12. Duch, W., Adamczak, R., Hayashi, Y.: Eliminators and classifiers. In: Lee, S.Y. (ed.) 7th International Conference on Neural Information Processing (ICONIP), Dae-jong, Korea, pp. 1029-1034 (2000)
13. Duch, W.: Filter methods. In: Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L. (eds.) Feature extraction, foundations and applications, pp. 89-118. Physica Verlag/Springer, Heidelberg (2006)
14. Schapire, R., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Machine Learning 37, 297-336 (1999)
15. Duch, W., Jankowski, N.: Survey of neural transfer functions. Neural Computing Surveys 2, 163-213 (1999)
16. Huang, G.-B., Chen, L., Siew, C.K.: Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks 17, 879-892 (2006)
17. Asuncion, A., Newman, D.: UCI machine learning repository (2007)
18. Golub, T.: Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286, 531-537 (1999)
19. Wolberg, W.H., Mangasarian, O.: Multisurface method of pattern separation for medical diagnosis applied to breast cytology. In: Proceedings of the National Academy of Sciences, U.S.A., vol. 87, pp. 9193-9196 (1990)
20. 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)
21. 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)