A Learning Algorithm based on High School Teaching Wisdom

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Philip, Ninan Sajeeth;
  • Subject: Computer Science - Artificial Intelligence | Computer Science - Learning

A learning algorithm based on primary school teaching and learning is presented. The methodology is to continuously evaluate a student and to give them training on the examples for which they repeatedly fail, until, they can correctly answer all types of questions. This... View more
  • References (24)
    24 references, page 1 of 3

    [1] T Hediger, M Wann, N. N. Greenbaun, The inuence of training sets on generalization in feedforward neural networks, International Joint Conf. on Neural Networks,3 1990,137{142

    [3] K. Keeni, H. Shimodaira, On selection of training data for fast learning of neural networks using back propagation, Arti cial Intelligence and Applications, (2002),362{067

    [4] Sethu Vijayakumar, Masashi Sugiyama, Hidemitsu Ogawa, Training data selection for optimal generalization with noise variance reduction in neural networks, Springer-Verlag, (1998), 153{166

    [5] R. Setiono, H. Liu, Neural network feature selector. IEEE Trans. Neural Networks, 8 (1997),654{661

    [6] Choueiki M.H, Mount-Campbell C.A, Training data development with the d-optimality criterion. IEEE Transactions on Neural Networks, 10[1](1999), 56{63

    [7] Masashi Sugiyama, Hidemitsu Ogawa, Training data selection for optimal generalization in trigonometric polynomial networks. Advances in Neural Information Processing Systems,MIT Press,(2000), 624{630

    [8] Kazuyuki Hara, Kenji Nakayama, A training method with small computation for classication. IEEE-INNS-ENNS International Joint Conference, 2000, 543{548.

    [9] Jigang Wang, Predrag Neskovic, L.N. Leon N Cooper, Training data selection for support vector machines. ICNC 2005 LNCS. Springer, 3610(2005), 554{564

    [10] Jose Ramon Cano, Francisco Herrera, Manuel Lozano, On the combination of evolutionary algorithms and strati ed strategies for training set selection in data mining. Applied Soft Computing, (2006),323{332

    [2] Slava M. Katz, Estimation of probabilities from sparse data for the language model component of a speech recognizer. IEEE Transactions on Acoustics, Speech and Signal Process- [12] D.L. Wilson, Asymptotic properties of nearing, (1987),400{401 est neighbor rules using edited data sets. IEEE

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