• Martin Anthony, Decision Lists and Related Classes of Boolean Functions. In Boolean Functions: Boolean Models and Methods in Mathematics, Computer Science, and Engineering  (ed. Y. Crama and P.L. Hammer) Cambridge University Press, 2010. (ISBN 9780521847520)

• Martin Anthony, Probabilistic Learning of Boolean Functions. In Boolean Functions: Boolean Models and Methods in Mathematics, Computer Science, and Engineering  (ed. Y. Crama and P.L. Hammer) Cambridge University Press, 2010. (ISBN 9780521847520)

• Martin Anthony, Neural Networks and Boolean Functions. In Boolean Functions: Boolean Models and Methods in Mathematics, Computer Science, and Engineering  (ed. Y. Crama and P.L. Hammer) Cambridge University Press, 2010. (ISBN 9780521847520)

• Martin Anthony and Joel Ratsaby. Maximal width learning of binary functions. Theoretical Computer Science, 411 (2010): 138-147. (pdf)

• Mine Subasi, Ersoy Subasi, Martin Anthony and Peter L. Hammer. A new imputation method for incomplete binary data. RUTCOR Research Report RRR 15-2009, August 2009, RUTCOR, Rutgers Center for Operations Research.  (pdf)
  

• Martin Anthony. On constructing threshold networks for pattern classification. In Constructive Neural Network Algorithms (eds. Leonardo Franco, David Elizondo, Jose Jerez), Springer Studies in Computational Intelligence Vol. 258, Springer, 2009.

• Mine Subasi, Ersoy Subasi, Martin Anthony and Peter L. Hammer. Using a similarity measure for credible classification. Discrete Applied Mathematics 157(5, 6), 1104-1112, 2009.

• Martin Anthony. Aspects of  discrete mathematics and probability in the theory of machine learning. Discrete Applied Mathematics 156(6), 883-902, 2008.
  

• Martin Anthony. On the generalization error of fixed combinations of classifiers. Journal of Computer and System Sciences, 73 (5), 725-734, 2007. [Earlier version: (pdf |  ps)]

• Martin Anthony, Advanced Mathematical Analysis, University of London Press 2007.

• Martin Anthony and Peter L. Hammer, A Boolean measure of similarity. Discrete Applied Mathematics 154 (16), 2006. [Earlier version: (pdf | ps)]

• Leonardo Franco and Martin Anthony. The influence of oppositely classified examples on the generalization complexity of Boolean functions. IEEE Transactions on Neural Networks, 17 (3), 2006, 578-590.
  

• Leonardo Franco, Jose Luis Subirats, Martin Anthony and Jose Jerez. A new constructive approach for creating all linearly separable (threshold) functions. Proceedings of the 2006 IEEE International Joint Conference on Neural Networks.

• Martin Anthony, Abstract Mathematics, University of London Press 2006.

• Martin Anthony and Michele Harvey, Advanced Linear Algebra, University of London Press 2006.

• Martin Anthony, On data classification by iterative linear partitioning. Discrete Applied Mathematics, 144 (1-2), 2-16, 2004.
  

• Martin Anthony, On learning a function of perceptrons. Proceedings of the 2004 IEEE International Joint Conference on Neural Networks, 2004.
  

• Leonardo Franco and Martin Anthony, On a generalization complexity measure for Boolean functions. Proceedings of the 2004 IEEE International Joint Conference on Neural Networks, 2004.

• Martin Anthony, Some connections between learning and optimization. Discrete Applied Mathematics, 144 (1-2), 17-26, 2004. 

• Martin Anthony, Partitioning Points by Parallel Planes.  Discrete Mathematics, Vol 282 (2004).
  

• Martin Anthony,  Generalization Error Bounds for Threshold Decision Lists. Journal of Machine Learning Research  5 (2004): 189-217, February 2004 (pdf | ps)
  

• Martin Anthony, Data Classification by Multithreshold Functions. Proceedings of the Workshop on Discrete Mathematics and Data Mining, 3^rd SIAM Conference on Data Mining, San Francisco, May 2003.

• Martin Anthony, Accuracy of Classification by Iterative Linear Thresholding. Proceedings of the Workshop on Discrete Mathematics and Data Mining, 3^rd SIAM Conference on Data Mining, San Francisco, May 2003.

• Martin Anthony and Norman Biggs,  PAC learning and artificial neural networks. In The Handbook of Brain Theory and Neural Networks, Second Edition, (ed. Michael A. Arbib), Bradford Books/MIT Press, November 2002.

• Martin Anthony, Uniform Glivenko-Cantelli Theorems and Concentration of Measure in the Mathematical Modelling of Learning. CDAM Research Report LSE-CDAM-2002-07, May 2002. (pdf)

• Martin Anthony and Norman Biggs, Matemática Para la Economía y las Finanzas, Cambridge University Press (Spanish translation of Mathematics for Economics and Finance)  (January 2002. ISBN: 848323248)

• Martin Anthony, Mathematical Modelling of Generalization. In M. Marinaro, R. Tagliaferri (eds.), Neural Nets: 13th ItalianWorkshop on Neural Nets, WIRN VIETRI 2002 Vietri sul Mare, Italy, May 30 – June 1, 2002. Springer LNCS 2486, 2002. (pdf)

• Martin Anthony, The Classification of Undergraduate Degrees in the United Kingdom: An Analysis of Problems with the Honours System. MA Research Report, Institute of Education, 2002.

• Martin Anthony, Mathematics 1, University of London Press 2002.

• Martin Anthony, Mathematics 2, University of London Press, 2002.

• Martin Anthony, Discrete Mathematics of Neural Networks: Selected Topics. SIAM Monographs on Discrete Mathematics and Applications, Number 8, SIAM (Society for Industrial and Applied Mathematics), Philadelphia, USA, 2001. (ISBN 0 89871 480 X)

• Martin Anthony and Peter Bartlett, Function learning from interpolation. Combinatorics, Probability and Computing, 2000 (pdf | ps)

• Martin Anthony and Peter Bartlett, Neural Network Learning: Theoretical Foundations, Cambridge University Press, Cambridge, UK, 1999.  (ISBN 0 521 57353 X.)
  

• Martin Anthony, Accuracy of techniques for the logical analysis of data. Discrete Applied Mathematics 96, 247--257, 1999. (pdf | ps) (Also, RUTCOR Research Report RRR 23-96, RUTCOR, Rutgers Center for Operations Research.)

• Martin Anthony. Further Mathematics for Economists (a subject guide for External Programmes) , 1999, Univ. London.

• Martin Anthony and Sean B. Holden, Cross-validation for binary classification by real-valued functions. In Proceedings of the 1998 Annual Conference on Computational Learning Theory.(COLT’98), Madison, Wisconsin, 1998. ACM Press. Full version: (pdf | ps)

• Martin Anthony, R. Hewins and C. Phillips, Quantitative Methods, (a subject guide for External Programmes), 1998, Univ. London; ISBN: 0718715209.

• John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson and Martin Anthony, Structural risk minimization over data-dependent hierarchies. IEEE Transactions on Information Theory. 44(5), 1926--1940, 1998. (ps)

• Martin Anthony, Probabilistic `generalization' of functions and dimension-based uniform convergence results. Statistics and Computing, 8(1), 5--14, 1998. (pdf | ps)

• Martin Anthony, Artificial neural networks. In Graph Connections (ed. Lowell Beineke and Robin Wilson). Oxford University Press, 1997. ((pdf | ps), without figures)

• Martin Anthony, Probabilistic analysis of learning in artificial neural networks: the PAC model and its variants. Neural Computing Surveys, 1, 1--47, 1997. (html | pdf | ps.gz)

• Martin Anthony. Mathematics for Economists (a subject guide for External Programmes), June 1997, Univ. London; ISBN: 07187144311997.

• Martin Anthony and John Shawe-Taylor, A sufficient condition for polynomial distribution-dependent learnability. Discrete Applied Mathematics, 77(1), 1--12, 1997. (pdf | ps)

• John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson and Martin Anthony, A framework for structural risk minimisation. In Proceedings of the 1996 Annual Conference on Computational Learning Theory (COLT'96), 68--76. ACM Press, 1996.

• Martin Anthony, Peter Bartlett, Yuval Ishai and John Shawe-Taylor, Valid generalisation from approximate interpolation. Combinatorics, Probability and Computing, Volume 5, 191--214 (1996). (pdf | ps)

• Martin Anthony and Norman Biggs. Mathematics for Economics and Finance: Methods and Modelling. Cambridge University Press, Cambridge, UK, 1996. (h/b ISBN 0 521 55913 8, p/b ISBN 0 521 55113 7). Reprinted several times, with Japanese and Chinese versions. 

• Martin Anthony, Threshold functions, decision lists, and the representation of Boolean functions. Neurocolt Technical Report NC-TR-96-028. (ps.gz)

• Martin Anthony, Mathematics (for Diploma in Economics), (a subject guide for External Programmes), May 1996, Univ. London; ISBN: 0718713508.

• Martin Anthony and Norman Biggs, A computational learning theory view of economic forecasting with neural nets. In Neural Networks in the Capital Markets, (ed. A.N. Refenes), Wiley, 1995.

• Martin Anthony, Interpolation and learning in artificial neural networks. In Proceedings of the 1995 IEEE International Conference on Neural Networks, Perth, Western Australia. IEEE Press.

• Martin Anthony, Graham Brightwell and John Shawe-Taylor, On specifying Boolean functions by labelled examples. Discrete Applied Mathematics, 61 (1995): 1--25. (pdf | ps)

• Martin Anthony, Classification by polynomial surfaces. Discrete Applied Mathematics, 61 (1995): 91--103.(pdf | ps)

• Martin Anthony and Peter Bartlett, Function learning from interpolation. In Proceedings EuroCOLT'95, Springer-Verlag, (1995): 211--221.

• Martin Anthony, Graham Brightwell and Colin Cooper, On the Vapnik-Chervonenkis dimension of a random graph. Discrete Mathematics 138 (1995): 43--56. (pdf | ps)

• Martin Anthony and Norman Biggs, Pac learning and artificial neural networks. In The Handbook of Brain Theory and Neural Networks (ed. Michael A Arbib) Bradford Books/MIT Press, 1995. (pdf | ps)

• Martin Anthony and John Shawe-Taylor, Valid generalisation of functions from close approximations on a sample. In Computational Learning Theory: EuroCOLT'93, 95--108, Oxford University Press 1994.

• Martin Anthony, Probabilistic learning theory, with emphasis on sample complexity. Report 94-002, Sonderforschungsbereich 343, Diskrete Structuren in der Mathematik, Universitat Bielefeld, 1994.

• Martin Anthony and Sean B. Holden, Quantifying generalisation in linearly weighted neural networks. Complex Systems 8, (1994), 91--114. (pdf | ps)

• Martin Anthony, On the mean chromatic number, Discrete Mathematics, Volume 125 (1994): 11--14.

• John Shawe-Taylor and Martin Anthony (eds.), Computational Learning Theory: EUROCOLT'93. Oxford University Press, 1994. (ISBN 0 19 853492 2)

• Martin Anthony and Norman Biggs, Computational learning theory for artificial neural networks. In Mathematical Approaches to Neural Networks (ed. J.G. Taylor), North-Holland Mathematical Library, North-Holland (Elsevier), 1993, 25--63. (pdf | ps)

• Martin Anthony and John Shawe-Taylor, Bounds on the complexity of testing and loading neurons. In ICANN'93: Proceedings of the International Conference on Artificial Neural Networks, 1993. Springer-Verlag.

• Martin Anthony and Sean B. Holden, On the power of linearly weighted neural networks. In ICANN'93: Proceedings of the International Conference on Artificial Neural Networks, 1993. Springer-Verlag.

• Martin Anthony and John Shawe-Taylor, Using the perceptron algorithm to find consistent hypotheses. Combinatorics, Probability and Computing, 4(2) (1993): 385--387. (pdf | ps)

• Martin Anthony and Sean B. Holden, On the power of polynomial discriminators and radial basis function networks. In Proceedings of the Sixth Annual Workshop on Computational Learning Theory, Santa Cruz, CA, 1993, 158--164, ACM Press.

• Martin Anthony and John Shawe-Taylor, A result of Vapnik with applications, Discrete Applied Mathematics, 47 (1993): 207--217. (pdf | ps)

• John Shawe-Taylor, Martin Anthony and Norman Biggs, Bounding sample-size with the Vapnik-Chervonenkis dimension, Discrete Applied Mathematics, 42 (1), 1993, 65--73. (pdf | ps)

• Martin Anthony and Norman Biggs, The mean chromatic number of paths and cycles. Discrete Mathematics 120 (1993): 227-231.

• Martin Anthony and Norman Biggs, Computational Learning Theory: An Introduction, Cambridge University Press, Cambridge, UK, 1992. Reprinted in paperback, 1997  (h/b ISBN 0 521 59922 9, p/b ISBN 0 521 41603 5.)

• John Shawe-Taylor, Martin Anthony and Walter Kern, Classes of feedforward neural networks and their circuit complexity, Neural Networks 5 (6), 1992: 971--977.

• Martin Anthony, Graham Brightwell, Dave Cohen and John Shawe-Taylor. On exact specification by examples. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, Santa Cruz, CA, 1992, 311-318, ACM Press.

• Martin Anthony, On deviation of relative frequencies from probabilities. LSE Mathematics Preprint Series LSE-MPS-2, 1991.

• John Shawe-Taylor and Martin Anthony, Sample sizes for multiple output threshold networks, Network: Computation in Neural Systems, 2 (1991): 107--117.

• Martin Anthony,  Uniform Convergence and Learnability. PhD thesis.,University of London 1991.
  

• Martin Anthony, Norman Biggs and John Shawe-Taylor, The learnability of formal concepts, in COLT 90, Proceedings of the Third Annual Workshop on Computational Learning Theory, Rochester NY, August 1990, 246--257. Published by Morgan Kauffman.

• Martin Anthony, Keith Martin, Jennifer Seberry and Peter Wild. Some remarks on authentication systems. In Advances in Cryptology: Auscrypt'90, Lecture Notes in Computer Science, 453 (1990), 122--139.

• Martin Anthony, Computing chromatic polynomials, Ars Combinatoria 90 (1990), 216-220.

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