ilh@sun-of-pooh.mit.edu (I. Lee Hetherington) (12/12/90)
Hello, Are there any good "incremental" training schemes for multi-layer perceptrons. By incremental I mean: once I use a particular training example to perform an update I throw it away and never use it again. I rely on a steady stream of new training data. The most obvious thing to me is simply to perform some small weight update using back-prop, but this is likely to require a huge stream of training data. Are there any techniques for updating weights using probabilistic methods akin to Bayesian Learning? For example, I assume some distribution for each weight. When I see an new training example, I use this example to update the distribution for each weight... Any references would be greatly appreciated! (Can you email them to me as I am an infrequent reader of this group, but post them if you think they are of general interest.) Thanks in advance! ------------------------------------------------------------------------------- Lee Hetherington MIT Spoken Language Systems Group ilh@goldilocks.lcs.mit.edu -------------------------------------------------------------------------------