Anthony, Martin

Number of items: 76.
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  • Anthony, Martin, Ratsaby, Joel (2014). A hybrid classifier based on boxes and nearest neighbors. Discrete Applied Mathematics, 172, 1-11. https://doi.org/10.1016/j.dam.2014.02.018
  • Anthony, Martin, Ratsaby, Joel (2014). Learning bounds via sample width for classifiers on finite metric spaces. Theoretical Computer Science, 529, 2-10. https://doi.org/10.1016/j.tcs.2013.07.004
  • Anthony, Martin, Ratsaby, Joel (2013). Maximal-margin case-based inference. In Jin, Yaochu, Thomas, Spencer Angus (Eds.), 2013 13th Uk Workshop on Computational Intelligence (Ukci): Management School Foyer, University of Surrey, Guildford, Surrey, Uk (pp. 112-119). IEEE. https://doi.org/10.1109/UKCI.2013.6651295
  • Anthony, Martin, Ratsaby, Joel (2013). Quantifying accuracy of learning via sample width. In 2013 IEEE Symposium on Foundations of Computational Intelligence (FOCI) (pp. 84-90). IEEE. https://doi.org/10.1109/FOCI.2013.6602459
  • Anthony, Martin, Ratsaby, Joel (2013). Large margin case-based reasoning. (RUTCOR Research Reports RRR 2-2013). Rutgers, The State University of New Jersey.
  • Anthony, Martin, Ratsaby, Joel (2012). Robust cutpoints in the logical analysis of numerical data. Discrete Applied Mathematics, 160(4 - 5), 355-364. https://doi.org/10.1016/j.dam.2011.07.014
  • Anthony, Martin, Ratasaby, Joel (2012). Analysis of a multi-category classifier. Discrete Applied Mathematics, 160(16-17), 2329-2338. https://doi.org/10.1016/j.dam.2012.07.010
  • Anthony, Martin, Ratsaby, Joel (2012). Sample width for multi-category classifiers. (RUTCOR Research Reports RRR 29-2012). RUTCOR, Rutgers University.
  • Anthony, Martin, Harvey, Michele (2012). Linear algebra: concepts and methods. Cambridge University Press.
  • Anthony, Martin, Ratsaby, Joel (2012). Learning on finite metric spaces. (RUTCOR research reports RRR 19-2012). Center for Operations Research, Rutgers University.
  • Anthony, Martin, Ratsaby, Joel (2012). Using boxes and proximity to classify data into several categories. (RUTCOR research reports RRR 7-2012). Center for Operations Research, Rutgers University.
  • Subasi, Munevver Mine, Subasi, Ersoy, Anthony, Martin, Hammer, Peter L. (2012-01-09 - 2012-01-11) A new imputation method for incomplete binary data [Paper]. International Symposium on Artificial Intelligence and Mathematics, Fort Lauderdale FL, United States, USA.
  • Anthony, Martin, Ratsaby, Joel (2012-01-09 - 2012-01-11) The performance of a new hybrid classifier based on boxes and nearest neighbors [Paper]. International Symposium on Artificial Intelligence and Mathematics, Fort Lauderdale FL, United States, USA.
  • Anthony, Martin (2012). Probability in machine learning. In Seel, Norbert M. (Ed.), Encyclopedia of the Sciences of Learning . Springer Berlin / Heidelberg.
  • Anthony, Martin, Harvey, Michele (2012). Further linear algebra.
  • Anthony, Martin (2012). Generalization error bounds for the logical analysis of data. Discrete Applied Mathematics, 160(10-11), 1407-1415. https://doi.org/10.1016/j.dam.2011.12.001
  • Anthony, Martin, Ratsaby, Joel (2011). The performance of a new hybrid classifier based on boxes and nearest neighbors. (RUTCOR research reports RRR 17-2011). Center for Operations Research, Rutgers University.
  • Subasi, Munevver Mine, Subasi, Ersoy, Anthony, Martin, Hammer, Peter L. (2011). A new imputation method for incomplete binary data. Discrete Applied Mathematics, 159(10), 1040-1047. https://doi.org/10.1016/j.dam.2011.01.024
  • Anthony, Martin (2011). Generalization error bounds for the logical analysis of data. (Rutcor Research Report 1-2011). Rutgers University.
  • Anthony, Martin, Harvey, Michele (2011). Algebra.
  • Anthony, Martin (2011). The beauty of maths.
  • Anthony, Martin (2010). Neural networks and boolean functions. In Crama, Yves, Hammer, Peter L. (Eds.), Boolean Models and Methods in Mathematics, Computer Science, and Engineering (pp. 554-576). Cambridge University Press.
  • Anthony, Martin (2010). Probabilistic learning of boolean functions. In Grama, Yves, Hammer, Peter L. (Eds.), Boolean Models and Methods in Mathematics, Computer Science, and Engineering (pp. 197-220). Cambridge University Press.
  • Anthony, Martin (2010). Decision lists and related classes of boolean functions. In Crama, Yves, Hammer, Peter L. (Eds.), Boolean Models and Methods in Mathematics, Computer Science, and Engineering (pp. 577-598). Cambridge University Press.
  • Anthony, Martin, Ratsaby, Joel (2010). Maximal width learning of binary functions. Theoretical Computer Science, 411(1), 138-147. https://doi.org/10.1016/j.tcs.2009.09.020
  • Anthony, Martin (2009). On constructing threshold networks for pattern classification. In Franco, Leonardo, Elizondo, David A., Jerez, José M. (Eds.), Constructive Neural Networks (pp. 71-82). Springer Berlin / Heidelberg.
  • Subasi, M., Subasi, E., Anthony, Martin, Hammer, P. L. (2009). Using a similarity measure for credible classification. Discrete Applied Mathematics, 157(5), 1104-1112. https://doi.org/10.1016/j.dam.2008.04.007
  • Subasi, Mine, Subasi, Ersoy, Anthony, Martin, Hammer, P.L. (2009). A new imputation method for incomplete binary data. (RUTCOR research report RRR 15-2009). RUTCOR, Rutgers University.
  • Anthony, Martin (2008). Aspects of discrete mathematics and probability in the theory of machine learning. Discrete Applied Mathematics, 156(6), 883-902. https://doi.org/10.1016/j.dam.2007.05.040
  • Anthony, Martin (2007). On the generalization error of fixed combinations of classifiers. Journal of Computer and System Sciences, 73(5), 725-734. https://doi.org/10.1016/j.jcss.2006.10.017
  • Anthony, Martin, Ratsaby, Joel (2006). Maximal width learning of binary functions. (CDAM research report series CDAM-LSE-2006-11). Centre for Discrete and Applicable Mathematics, London School of Economics and Political Science.
  • Anthony, Martin (2006). Abstract mathematics. University of London Press.
  • Anthony, Martin, Harvey, M (2006). Advanced linear algebra. University of London Press.
  • Anthony, Martin, Hammer, Peter Ladislaw (2006). A Boolean measure of similarity. Discrete Applied Mathematics, 154(16), 2242-2246. https://doi.org/10.1016/j.dam.2006.04.011
  • Franco, L., Subirats, J.L., Anthony, Martin, Jerez, J.M. (2006). A new constructive approach for creating all linearly separable (threshold) functions. In International Joint Conference on Neural Networks, 2006. Ijcnn '06 (pp. 4791-4796). IEEE. https://doi.org/10.1109/IJCNN.2006.247155
  • Anthony, Martin (2005). Connections between neural networks and boolean functions. (CDAM research report series CDAM-LSE-2005-25). Centre for Discrete and Applicable Mathematics, London School of Economics and Political Science.
  • Anthony, Martin (2005). Decision lists. (CDAM research report series CDAM-LSE-2005-23). Centre for Discrete and Applicable Mathematics, London School of Economics and Political Science.
  • Anthony, Martin (2005). Learning boolean functions. (CDAM research report series CDAM-LSE-2005-24). Centre for Discrete and Applicable Mathematics, London School of Economics and Political Science.
  • Anthony, Martin, Hammer, P. L., Subasi, E., Subasi, M. (2005). Using a similarity measure for credible classification. (CDAM research report series CDAM-LSE-2005-22). Centre for Discrete and Applicable Mathematics, London School of Economics and Political Science.
  • Anthony, Martin (2004). Some connections between learning and optimization. Discrete Applied Mathematics, 144(1-2), 17-26. https://doi.org/10.1016/j.dam.2004.06.005
  • Anthony, Martin (2004). Partitioning points by parallel planes. Discrete Mathematics, 282(1-3), 17-21.
  • Anthony, Martin (2004). Generalization error bounds for threshold decision lists. Journal of Machine Learning Research, 5(Feb), 189-217.
  • Anthony, Martin, Hammer, Peter L. (2004). A Boolean measure of similarity. (RUTCOR research reports 27-2004). Rutgers Center for Operations Research, Rutgers University.
  • Anthony, Martin (2004). On Learning a Function of Perceptrons. In Proceedings of the IEEE 2004 International Joint Conference on Neural Networks (pp. 967-972). IEEE Press. https://doi.org/10.1109
  • Franco, Leonardo, Anthony, Martin (2004). On a generalisation complexity measure for boolean functions. In Proceedings of the IEEE 2004 International Joint Conference on Neural Networks . IEEE Press.
  • Anthony, Martin (2004). On data classification by iterative linear partitioning. Discrete Applied Mathematics, 144(1-2), 2-16.
  • Anthony, Martin (2003). On Boolean combinations of definitive classifiers. (CDAM Research Report Series LSE-CDAM-2003-22). Centre for Discrete and Applicable Mathematics, London School of Economics and Political Science.
  • Franco, Leonardo, Anthony, Martin (2003). The influence of opposite examples and randomness on the generalization complexity of Boolean functions. (CDAM research report series LSE-CDAM-2003-21). Centre for Discrete and Applicable Mathematics, London School of Economics and Political Science.
  • Anthony, Martin (2003-05-01 - 2003-05-03) Accuracy of classification by iterative linear thresholding [Paper]. SIAM International Conference on Data Mining, CA., United States, USA.
  • Anthony, Martin (2003-05-01 - 2003-05-03) Data classification by multithreshold functions [Paper]. SIAM International Conference on Data Mining, CA., United States, USA.
  • Anthony, Martin (2003). Margin-based generalization error bounds for threshold decision lists. (CDAM research report series LSE-CDAM-2003-09). Centre for Discrete and Applicable Mathematics, London School of Economics and Political Science.
  • Anthony, Martin (2003). Boolean functions and artificial neural networks. (CDAM research report series LSE-CDAM-2003-01). Centre for Discrete and Applicable Mathematics, London School of Economics and Political Science.
  • Anthony, Martin (2003). Learning multivalued multithreshold functions. (CDAM Research Report Series LSE-CDAM-2003-03). Centre for Discrete and Applicable Mathematics, London School of Economics and Political Science.
  • Anthony, Martin (2003). Links between learning and optimization: a brief tutorial. (CDAM research report series LSE-CDAM-2003-02). Centre for Discrete and Applicable Mathematics, London School of Economics and Political Science.
  • Anthony, Martin (2002). Analysis of data with threshold decision lists. (CDAM research report series LSE-CDAM-2002-12). Centre for Discrete and Applicable Mathematics, London School of Economics and Political Science.
  • Anthony, Martin (2002). Decision lists and threshold decision lists. (CDAM research report series LSE-CDAM-2002-11). Centre for Discrete and Applicable Mathematics, London School of Economics and Political Science.
  • Anthony, Martin (2002). The sample complexity and computational complexity of Boolean function learning. (CDAM research report series LSE-CDAM-2002-13). Centre for Discrete and Applicable Mathematics, London School of Economics and Political Science.
  • Anthony, Martin (2002). Partitioning points by parallel planes. (CDAM research report series LSE-CDAM-2002-10). Centre for Discrete and Applicable Mathematics, London School of Economics and Political Science.
  • Anthony, Martin (2002). Uniform Glivenko-Cantelli theorems and concentration of measure in the mathematical modelling of learning. (CDAM research report series LSE-CDAM-2002-07). Centre for Discrete and Applicable Mathematics, London School of Economics and Political Science.
  • Anthony, Martin (2002). Mathematical modelling of generalization. In Marinaro, M,, Tagliaferri, R. (Eds.), Neural Nets: 13th Italian Workshop on Neural Nets, Wirn Vietri 2002 Vietri Sul Mare, Italy, May 30-June 1, 2002 (pp. 181-189). Springer Berlin / Heidelberg.
  • Anthony, Martin (2001). Discrete mathematics of neural networks: selected topics. Society for Industrial and Applied Mathematics.
  • Anthony, Martin (1999). Accuracy of techniques for the logical analysis of data. Discrete Applied Mathematics, 96-97(1-3), 247-257. https://doi.org/10.1016/S0166-218X(99)00094-3
  • Anthony, Martin, Bartlett, P (1999). Neural network learning: theoretical foundations. Cambridge University Press.
  • Anthony, Martin, Biggs, Norman (1995). Pac learning and artificial neural networks. In Arbib, Michael A. (Ed.), Handbook of Brain Theory and Neural Networks (pp. 694-697). MIT Press.
    • Public
  • Anthony, Martin, Ratsaby, Joel (2020). Large-width machine learning algorithm. Progress in Artificial Intelligence, 9(3), 275 – 285. https://doi.org/10.1007/s13748-020-00212-4 picture_as_pdf
  • Anthony, Martin, Ratsaby, Joel (2018). Large width nearest prototype classification on general distance spaces. Theoretical Computer Science, 738(22), 65-79. https://doi.org/10.1016/j.tcs.2018.04.045
  • Anthony, Martin, Ratsaby, Joel (2018). Large-width bounds for learning half-spaces on distance spaces. Discrete Applied Mathematics, https://doi.org/10.1016/j.dam.2018.02.004
  • Anthony, Martin, Ratsaby, Joel (2017). Classification based on prototypes with spheres of influence. Information and Computation, 256, 372-380. https://doi.org/10.1016/j.ic2017.08.004
  • Anthony, Martin, Boros, Endre, Crama, Yves, Gruber, Aritanan (2017). Quadratic reformulations of nonlinear binary optimization problems. Mathematical Programming, 162(1), 115-144. https://doi.org/10.1007/s10107-016-1032-4
  • Anthony, Martin, Ratsaby, Joel (2016). Multi-category classifiers and sample width. Journal of Computer and System Sciences, 82(8), 1223-1231. https://doi.org/10.1016/j.jcss.2016.04.003
  • Anthony, Martin, Boros, Endre, Crama, Yves, Gruber, Aritanan (2016). Quadratization of symmetric pseudo-Boolean functions. Discrete Applied Mathematics, 203, 1-12. https://doi.org/10.1016/j.dam.2016.01.001
  • Anthony, Martin, Ratsaby, Joel (2015). A probabilistic approach to case-based inference. Theoretical Computer Science, 589, 61-75. https://doi.org/10.1016/j.tcs.2015.04.016
  • Anthony, Martin, Boros, Endre, Crama, Yves, Gruber, Aritanan (2013). Quadratization of symmetric pseudo-Boolean functions. (RUTCOR Research Reports RRR 12-2013). Rutgers University, Rutgers Center for Operations Research.
  • Anthony, Martin, Franco, Leonardo (2006). The influence of oppositely classified examples on the generalization complexity of Boolean functions. IEEE Transactions on Neural Networks, 17(3), 578-590. https://doi.org/10.1109/TNN.2006.872352
  • Anthony, Martin, Bartlett, Peter L. (2000). Function learning from interpolation. Combinatorics, Probability and Computing, 9(3), 213-225.
  • Anthony, Martin Henry George (1991). Uniform convergence and learnability. [Doctoral thesis]. London School of Economics and Political Science.