Neural net paper available by anonymous ftp

[Mario Marchand]

The following paper, which was presented at the NIPS'94 conference,
is available by anonymous ftp at:

ftp://dirac.physics.uottawa.ca/usr2/ftp/pub/tr/marchand


FileName: nips94.ps

Title: Learning Stochastic Perceptrons Under k-Blocking Distributions

Authors: Marchand M. and Hadjifaradji S.

Abstract:
We present a statistical method
that PAC learns the class of stochastic
perceptrons with arbitrary monotonic activation function
and weights $w_i \in \{-1, 0, +1\}$
when the probability distribution that generates
the input examples is member of a family that we call {\em k-blocking
distributions\/}. Such distributions
represent an important step beyond the case
where each input variable is statistically independent
since the 2k-blocking family contains all the Markov
distributions of order k.
By stochastic perceptron we mean a perceptron which,
upon presentation of input
vector $\x$,  outputs~1 with probability  $f(\sum_i w_i x_i - \theta)$.
Because the same algorithm works for any monotonic
(nondecreasing or nonincreasing) activation function $f$
on Boolean domain,
it handles the well studied cases of sigmo\"{\i}ds
and the ``usual'' radial basis functions.

ALSO: you will find other papers co-authored by Mario Marchand in
      this directory. The text file: Abstracts-mm.txt contains a
      list of abstracts of all the papers.

PLEASE: communicate to me any printing or transmission problems.
        Any comments concerning these papers are very welcome.




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