John M Thorp

US Patent:

6415286, Jul 2, 2002

Filed:

Mar 29, 1999

Appl. No.:

09/281984

Inventors:

Anthony Passera - Watertown MA
John R. Thorp - Columbia MD
Michael J. Beckerle - Needham MA
Edward S. Zyszkowski - Newton MA

Assignee:

Torrent Systems, Inc. - Cambridge MA

International Classification:

G06F 15163

US Classification:

707 6, 712 11, 712 16, 712 10, 712 28, 345506, 345523

Abstract:

A computer system splits a data space to partition data between processors or processes. The data space may be split into sub-regions which need not be orthogonal to the axes defined the data spaces parameters, using a decision tree. The decision tree can have neural networks in each of its non-terminal nodes that are trained on, and are used to partition, training data. Each terminal, or leaf, node can have a hidden layer neural network trained on the training data that reaches the terminal node. The training of the non-terminal nodes neural networks can be performed on one processor and the training of the leaf nodes neural networks can be run on separate processors. Different target values can be used for the training of the networks of different non-terminal nodes. The non-terminal node networks may be hidden layer neural networks. Each non-terminal node automatically may send a desired ratio of the training records it receives to each of its child nodes, so the leaf node networks each receives approximately the same number of training records.

US Patent:

59096810, Jun 1, 1999

Filed:

Mar 25, 1996

Appl. No.:

8/624844

Inventors:

Anthony Passera - Watertown MA
John R. Thorp - Columbia MD
Michael J. Beckerle - Needham MA
Edward S. A. Zyszkowski - Newton MA

Assignee:

Torrent Systems, Inc. - Cambridge MA

International Classification:

G06F 15163

US Classification:

707 8

Abstract:

A computer system splits a data space to partition data between processors or processes. The data space may be split into sub-regions which need not be orthogonal to the axes defined by the data space's parameters, using a decision tree. The decision tree can have neural networks in each of its non-terminal nodes that are trained on, and are used to partition, training data. Each terminal, or leaf, node can have a hidden layer neural network trained on the training data that reaches the terminal node. The training of the non-terminal nodes' neural networks can be performed on one processor and the training of the leaf nodes' neural networks can be run on separate processors. Different target values can be used for the training of the networks of different non-terminal nodes. The non-terminal node networks may be hidden layer neural networks. Each non-terminal node automatically may send a desired ratio of the training records it receives to each of its child nodes, so the leaf node networks each receives approximately the same number of training records.