Publications about 'machine learning'
Articles in journal or book chapters
  1. B. DasGupta, H.T. Siegelmann, and E.D. Sontag. On the complexity of training neural networks with continuous activation functions. IEEE Trans. Neural Networks, 6:1490-1504, 1995. [PDF] Keyword(s): neural networks, analog computing, theory of computing, neural networks, computational complexity, machine learning.
    Blum and Rivest showed that any possible neural net learning algorithm based on fixed architectures faces severe computational barriers. This paper extends their NP-completeness result, which applied only to nets based on hard threshold activations, to nets that employ a particular continuous activation. In view of neural network practice, this is a more relevant result to understanding the limitations of backpropagation and related techniques.

  2. B. DasGupta, H.T. Siegelmann, and E.D. Sontag. On the Intractability of Loading Neural Networks. In V. P. Roychowdhury, Siu K. Y., and Orlitsky A., editors, Theoretical Advances in Neural Computation and Learning, pages 357-389. Kluwer Academic Publishers, 1994. [PDF] Keyword(s): analog computing, neural networks, computational complexity, machine learning.

Conference articles
  1. B. DasGupta, H. T. Siegelmann, and E.D. Sontag. On a learnability question associated to neural networks with continuous activations (extended abstract). In COLT '94: Proceedings of the seventh annual conference on Computational learning theory, New York, NY, USA, pages 47-56, 1994. ACM Press. [doi:] Keyword(s): analog computing, neural networks, computational complexity, machine learning.

  2. H.T. Siegelmann, E.D. Sontag, and C.L. Giles. The Complexity of Language Recognition by Neural Networks. In Proceedings of the IFIP 12th World Computer Congress on Algorithms, Software, Architecture - Information Processing '92, Volume 1, pages 329-335, 1992. North-Holland. Keyword(s): neural networks, computational complexity, machine learning, recurrent neural networks, theory of computing and complexity.

  3. T. Asano, J. Hershberger, J. Pach, E.D. Sontag, D. Souivaine, and S. Suri. Separating bi-chromatic points by parallel lines. In Proceedings of the Second Canadian Conf. on Computational Geometry, Ottawa, Canada, 1990, pages 46-49, 1990. [PDF] Keyword(s): computational geometry.
    Given a 2-coloring of the vertices of a regular n-gon P, how many parallel lines are needed to separate the vertices into monochromatic subsets? We prove that floor(n/2) is a tight upper bound, and also provide an O(n log n) time algorithm to determine the direction that gives the minimum number of lines. If the polygon is a non-regular convex polygon, then n-3 lines may be necessary, while n-2 lines always suffice. This problem arises in machine learning and has implications about the representational capabilities of some neural networks.



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