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COLT '91

Proceedings of the Fourth Annual Workshop, UC Santa Cruz, California, August 5-7, 1991

  • 1st Edition - July 1, 1991
  • Editor: COLT
  • Language: English
  • eBook ISBN:
    9 7 8 - 1 - 4 8 3 2 - 9 9 1 4 - 3

COLT '91: Proceedings of the Fourth Annual Workshop on Computational Learning Theory covers the papers presented at the Fourth Workshop on Computational Learning Theory, held at… Read more

COLT '91

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COLT '91: Proceedings of the Fourth Annual Workshop on Computational Learning Theory covers the papers presented at the Fourth Workshop on Computational Learning Theory, held at the University of California at Santa Cruz on August 5-7, 1991. The book focuses on quantitative theories of machine learning. The selection first offers information on the role of learning in autonomous robots; tracking drifting concepts using random examples; investigating the distribution assumptions in the PAC learning model; and simultaneous learning of concepts and simultaneous estimation of probabilities.The text then examines the calculation of the learning curve of Bayes optimal classification algorithm for learning a perceptron with noise and a geometric approach to threshold circuit complexity. The manuscript takes a look at learning curves in large neural networks, learnability of infinitary regular sets, and learning monotone DNF with an incomplete membership oracle. Topics include monotone DNF learning algorithm, difficulties in learning infinitary regular sets, learning of a perception rule, and annealed approximation. The book also examines the fast identification of geometric objects with membership queries and a loss bound model for on-line stochastic prediction strategies. The selection is a valuable source of information for researchers interested in the computational learning theory.