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    block this user Peter Sollich Trusted member

    Professor

    King's College London

    Finite Size Effects in on-Line Learning of Multi-Layer Neural Networks.

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    ions to the mean dynamics induced by finite dimensional inputs[3]. We assume that the teacher network the student attempts to learn is a soft committee machine[1] of N inputs, and M hidden units, this being a one hidden layer network with weights connecting each hidden to output unit set to +1, and with each hidden unit n connected to all input units by B n (n = 1::M ). Explicitly, for the N dimensional training input vector ¸ Ż , the output of the teacher is given by, i Ż = M X n=1 g(B n Delta ¸ Ż ); (1) where g(x) is the activation function of the hidden units, and we take g(x) = erf(

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    New Full text for this article was not available? Send a request to the author(s).
    Title : Finite Size Effects in on-Line Learning of Multi-Layer Neural Networks.
    Abstract : ions to the mean dynamics induced by finite dimensional inputs[3]. We assume that the teacher network the student attempts to learn is a soft committee machine[1] of N inputs, and M hidden units, this being a one hidden layer network with weights connecting each hidden to output unit set to +1, and with each hidden unit n connected to all input units by B n (n = 1::M ). Explicitly, for the N dimensional training input vector ¸ Ż , the output of the teacher is given by, i Ż = M X n=1 g(B n Delta ¸ Ż ); (1) where g(x) is the activation function of the hidden units, and we take g(x) = erf(
    Subject : unspecified
    Area : Mathematics
    Language : English
    Affiliations : 
    Url : http://ftp://cs.aston.ac.uk/neural/barberd/online.ps.Z
    Doi : 10.1.1.51.9258

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