Abstract:
:We have created a network that allocates a new computational unit whenever an unusual pattern is presented to the network. This network forms compact representations, yet learns easily and rapidly. The network can be used at any time in the learning process and the learning patterns do not have to be repeated. The units in this network respond to only a local region of the space of input values. The network learns by allocating new units and adjusting the parameters of existing units. If the network performs poorly on a presented pattern, then a new unit is allocated that corrects the response to the presented pattern. If the network performs well on a presented pattern, then the network parameters are updated using standard LMS gradient descent. We have obtained good results with our resource-allocating network (RAN). For predicting the Mackey-Glass chaotic time series, RAN learns much faster than do those using backpropagation networks and uses a comparable number of synapses.
journal_name
Neural Computjournal_title
Neural computationauthors
Platt Jdoi
10.1162/neco.1991.3.2.213subject
Has Abstractpub_date
1991-07-01 00:00:00pages
213-225issue
2eissn
0899-7667issn
1530-888Xjournal_volume
3pub_type
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