Learning parameters of linear models in compressed parameter space

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

Volumn 7553 LNCS, Issue PART 2, 2012, Pages 108-115

  Kassahun, Yohannes ^a   Wöhrle, Hendrik ^b   Fabisch, Alexander ^a   Tabie, Marc ^a  

^a UNIVERSITY OF BREMEN   (Germany)

^b GERMAN RESEARCH CENTER FOR ARTIFICIAL INTELLIGENCE DFKI   (Germany)

Author keywords

Compressed Sensing; Reinforcement Learning; Supervised Learning

Indexed keywords

COMPRESSIVE SENSING; INPUT SPACE; LEARNING PARAMETERS; PARAMETER SPACES; TRAINING TIME;

REINFORCEMENT LEARNING; SUPERVISED LEARNING;

NEURAL NETWORKS;

EID: 84867685120     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33266-1_14     Document Type: Conference Paper

References (15)

1. Calderbank, R., Jafarpour, S., Schapire, R.: Compressed learning: Universal sparse dimensionality reduction and learning in the measurement domain. Technical report (2009)
2. Davenport, M.A., Duarte, M.F., Wakin, M.B., Laska, J.N., Takhar, D., Kelly, K.F., Baraniuk, R.G.: The smashed filter for compressive classification and target recognition. In: Proceedings of Computational Imaging V at SPIE Electronic Imaging, San Jose, CA (January 2007)
3. Donoho, D.L.: Compressed sensing. IEEE Transactions on Information Theory 52(4), 1289-1306 (2006)
4. Gomez, F.J., Miikkulainen, R.: Robust non-linear control through neuroevolution. Technical Report AI-TR-03-303, Department of Computer Sciences, The University of Texas, Austin, USA (2002)
5. Gomez, F.J., Schmidhuber, J., Miikkulainen, R.: Efficient Non-linear Control Through Neuroevolution. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 654-662. Springer, Heidelberg (2006)
6. Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation 9(2), 159-195 (2001)
7. Haupt, J., Castro, R., Nowak, R., Fudge, G., Yeh, A.: Compressive sampling for signal classification. In: Proceedings of the 40th Asilomar Conference on Signals, Systems and Computers, pp. 1430-1434 (2006)
8. Kassahun, Y., de Gea, J., Edgington, M., Metzen, J.H., Kirchner, F.: Accelerating neuroevolutionary methods using a kalman filter. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation (GECCO), pp. 1397-1404. ACM, New York (2008)
9. Koutník, J., Gomez, F., Schmidhuber, J.: Searching for minimal neural networks in fourier space. In: Baum, E., Hutter, M., Kitzelnmann, E. (eds.) Proceedings of the Third Conference on Artificial General Intelligence (AGI), pp. 61-66. Atlantic Press (2010)
10. Koutník, J., Gomez, F.J., Schmidhuber, J.: Evolving neural networks in compressed weight space. In: Proceedings of Genetic and Evolutionary Computation Conference (GECCO), pp. 619-626. ACM, New York (2010)
11. Maillard, O., Munos, R.: Compressed least-squares regression. In: Bengio, Y., Schuurmans, D., Lafferty, J., Williams, C.K.I., Culotta, A. (eds.) Advances in Neural Information Processing Systems (NIPS), pp. 1213-1221 (2009)
12. Schaul, T., Schmidhuber, J.: Towards Practical Universal Search. In: Proceedings of the Third Conference on Artificial General Intelligence (AGI), Lugano (2010)
13. Velez, D.R., White, B.C., Motsinger, A.A., Bush, W.S., Ritchie, M.D., Williams, S.M., Moore, J.H.: A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Genetic Epidemiology 31(4), 306-315 (2007)
14. Zander, T.O., Kothe, C.: Towards passive brain computer interfaces: applying brain computer interface technology to human machine systems in general. Journal of Neural Engineering 8(2), 025005 (2011)
15. Zhou, S., Lafferty, J.D., Wasserman, L.A.: Compressed regression. In: Platt, J.C., Koller, D., Singer, Y., Roweis, S.T. (eds.) Advances in Neural Information Processing Systems (NIPS), Curran Associates, Inc. (2008)