Publication Details
Title: Learning Topology-Preserving Maps Using Self-Supervised Backpropagation on a Parallel Machine
Author: A. Ossen
Group: ICSI Technical Reports
Date: September 1992
PDF: ftp://ftp.icsi.berkeley.edu/pub/techreports/1992/tr-92-059.pdf
Overview:
Self-supervised backpropagation is an unsupervised learning procedure for feedforward networks, where the desired output vector is identical with the input vector. For backpropagation, we are able to use powerful simulators running on parallel machines. Topology-preserving maps, on the other hand, can be developed by a variant of the competitive learning procedure. However, in a degenerate case, self-supervised backpropagation is a version of competitive learning. A simple extension of the cost function of backpropagation leads to a competitive version of self-supervised backpropagation, which can be used to produce topographic maps. We demonstrate the approach applied to the Traveling Salesman Problem (TSP). The algorithm was implemented using the backpropagation simulator (CLONES) on a parallel machine (RAP).
Bibliographic Information:
ICSI Technical Report TR-92-059
Bibliographic Reference:
A. Ossen. Learning Topology-Preserving Maps Using Self-Supervised Backpropagation on a Parallel Machine. ICSI Technical Report TR-92-059, September 1992
Author: A. Ossen
Group: ICSI Technical Reports
Date: September 1992
PDF: ftp://ftp.icsi.berkeley.edu/pub/techreports/1992/tr-92-059.pdf
Overview:
Self-supervised backpropagation is an unsupervised learning procedure for feedforward networks, where the desired output vector is identical with the input vector. For backpropagation, we are able to use powerful simulators running on parallel machines. Topology-preserving maps, on the other hand, can be developed by a variant of the competitive learning procedure. However, in a degenerate case, self-supervised backpropagation is a version of competitive learning. A simple extension of the cost function of backpropagation leads to a competitive version of self-supervised backpropagation, which can be used to produce topographic maps. We demonstrate the approach applied to the Traveling Salesman Problem (TSP). The algorithm was implemented using the backpropagation simulator (CLONES) on a parallel machine (RAP).
Bibliographic Information:
ICSI Technical Report TR-92-059
Bibliographic Reference:
A. Ossen. Learning Topology-Preserving Maps Using Self-Supervised Backpropagation on a Parallel Machine. ICSI Technical Report TR-92-059, September 1992