BICA: A Boolean Indepenedent Component Analysis Approach (original) (raw)

References

1. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. In: Parallel Distrib. Proc., vol. 1, pp. 318–362. MIT Press, Cambridge (1987)
   Google Scholar
2. Kreutz-Delgado, K., Murray, J.F., Rao, B.D., Engan, K., Lee, T.W., Sejnowski, T.J.: Dictionary learning algorithms for sparse representation. Neural Computation 15(2), 349–396 (2003)
   Article MATH Google Scholar
3. Apolloni, B., Malchiodi, D., Brega, A.: BICA: a Boolean Independent Component Analysis Algorithm. Hybrid Intelligent System, 131–136 (2005)
   Google Scholar
4. Dietterich, T.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)
   Chapter Google Scholar
5. Apolloni, B., Esposito, A., Malchiodi, D., Orovas, C., Palmas, G., Taylor, J.: A general framework for learning rules from data. IEEE Transactions on Neural Networks 15(6) (2004)
   Google Scholar
6. Duda, R., Hart, P.: Pattern Classification and Scene Analysis. John Wiley & Sons, New York (1973)
   MATH Google Scholar
7. Bell, A.J., Sejnowski, T.J.: An information-maximization approach to blind separation and blind deconvolution. Neural Computation 7, 1129–1159 (1995)
   Article Google Scholar
8. Kreutz-Delgado, K., Rao, B.: Application of concave/Schur-concave functions to the learning of overcomplete dictionaries and sparse representations. In: Signals, Systems & Computers, 1998. Conference Record of the Thirty-Second Asilomar Conference, vol. 1, pp. 546–550 (1998)
   Google Scholar
9. Rockafellar, R.: Convex Analysis. Princeton University Press, Princetion (1970)
   MATH Google Scholar
10. Cover, T., Thomas, J.: Elements of Information Theory. Wiley & Sons, New York (1938)
    Google Scholar
11. Glivenko, V.: Sulla determinazione empirica delle leggi di probabilità. Giornale dell’Istituto Italiano degli Attuari 3, 92–99 (1933)
    Google Scholar
12. Delgado, K., Murray, J., Rao, B., Engan, K., Lee, T., Sejnowski, T.: Dictionary learning algorithms for sparse representation. Neural Computation 15, 349–396 (2003)
    Article MATH Google Scholar
13. Apolloni, B., Malchiodi, D., Orovas, C., Palmas, G.: From synapses to rules. Cognitive Systems Research 3, 167–201 (2002)
    Article Google Scholar
14. Pollack, J.: Recursive distributed representation. Artificial Intelligence 46, 77–105 (1990)
    Article Google Scholar
15. Koenig, S., Holte, R.C.: PAC Meditation on Boolean Formulas. In: Koenig, S., Holte, R.C. (eds.) Abstraction, Reformulation and Approximation. Springer, Berlin (2002)
    Google Scholar
16. Amaldi, E., Kann, V.: On the approximation of minimizing non zero variables or unsatisfied relations in linear systems. Theoretical Computer Science 209, 237–260 (1998)
    Article MATH MathSciNet Google Scholar
17. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
    MATH Google Scholar
18. Apolloni, B., Brega, A., Malchiodi, D., Palmas, G., Zanaboni, A.: Learning rule representations from data. IEEE Trans. on Systems, Man and Cybernetics, Part A 36(5), 1010–1028 (2006)
    Article Google Scholar
19. Asuncion, A., Newman, D.: UCI machine learning repository (2007)
    Google Scholar
20. Gorman, R.P., Sejnowski, T.J.: Analysis of hidden units in a layered network trained to classify sonar targets. Neural Networks 1, 75–89 (1988)
    Article Google Scholar
21. Alon, U., et al.: Broad patterns of gene expressions revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. PNAS 96, 6745–6750 (1999)
    Article Google Scholar
22. Cristianini, N., Shawe-Taylor, J.: An introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press, Cambridge (2000)
    Google Scholar
23. Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)
    Chapter Google Scholar
24. Ho, T.: The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)
    Article Google Scholar
25. Bobrowski, L., Lukaszuk, T.: Selection of the linearly separable feature subsets. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 544–549. Springer, Heidelberg (2004)
    Google Scholar
26. Furey, T., Cristianini, N., Duffy, N., Bednarski, D., Schummer, M., Haussler, D.: Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16(10), 906–914 (2000)
    Article Google Scholar

Download references