CA2203776A1 - Concurrent learning and performance information processing system - Google Patents

Concurrent learning and performance information processing system

Info

Publication number
CA2203776A1
CA2203776A1 CA002203776A CA2203776A CA2203776A1 CA 2203776 A1 CA2203776 A1 CA 2203776A1 CA 002203776 A CA002203776 A CA 002203776A CA 2203776 A CA2203776 A CA 2203776A CA 2203776 A1 CA2203776 A1 CA 2203776A1
Authority
CA
Canada
Prior art keywords
feature
values
measurement
missing
value
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CA002203776A
Other languages
French (fr)
Other versions
CA2203776C (en
Inventor
Robert J. Jannarone
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Netuitive Inc
Original Assignee
Robert J. Jannarone
Rapid Clip Neural Systems, Inc.
Netuitive Software, Inc.
Netuitive, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Robert J. Jannarone, Rapid Clip Neural Systems, Inc., Netuitive Software, Inc., Netuitive, Inc. filed Critical Robert J. Jannarone
Publication of CA2203776A1 publication Critical patent/CA2203776A1/en
Application granted granted Critical
Publication of CA2203776C publication Critical patent/CA2203776C/en
Anticipated expiration legal-status Critical
Expired - Fee Related legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/211—Selection of the most significant subset of features
    • G06F18/2113—Selection of the most significant subset of features by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/10—Interfaces, programming languages or software development kits, e.g. for simulating neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/771—Feature selection, e.g. selecting representative features from a multi-dimensional feature space

Abstract

At the beginning of each time trial a vector of measurement values and a vector of measurement plausibility values are supplied to a system (10), and a learning weight is either supplied to or generated by the system (10). The system (10) then performs the following operations during each time trial: convert-ing the measurement values to feature values; convert-ing the measurement plausibility values to feature vi-ability values; using each viability value to determine missing value status of each feature value; using non-missing feature values to update parameter learning;
imputing each missing feature value from non-missing feature values and/or prior learning; converting im-puted feature values to output imputed measurement values; and applying a variety of feature values and feature function monitoring and interpretation statis-tics. A parallel embodiment of the system (10) per-forms all such operations concurrently, through the coordinated use of parallel feature processors (31) and a joint access memory (23), which contains connec-tion weights and provision for connecting feature pro-cessors pairwise.
CA002203776A 1994-11-02 1995-11-01 Concurrent learning and performance information processing system Expired - Fee Related CA2203776C (en)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US333,204 1989-04-04
US08/333,204 US5835902A (en) 1994-11-02 1994-11-02 Concurrent learning and performance information processing system
PCT/US1995/014160 WO1996014616A1 (en) 1994-11-02 1995-11-01 Concurrent learning and performance information processing system

Publications (2)

Publication Number Publication Date
CA2203776A1 true CA2203776A1 (en) 1996-05-17
CA2203776C CA2203776C (en) 2002-10-08

Family

ID=23301789

Family Applications (1)

Application Number Title Priority Date Filing Date
CA002203776A Expired - Fee Related CA2203776C (en) 1994-11-02 1995-11-01 Concurrent learning and performance information processing system

Country Status (7)

Country Link
US (2) US5835902A (en)
EP (1) EP0789869A4 (en)
JP (1) JP2000511307A (en)
CN (1) CN1229482A (en)
CA (1) CA2203776C (en)
NZ (1) NZ296732A (en)
WO (1) WO1996014616A1 (en)

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Also Published As

Publication number Publication date
JP2000511307A (en) 2000-08-29
AU706727B2 (en) 1999-06-24
AU4141396A (en) 1996-05-31
US6289330B1 (en) 2001-09-11
US5835902A (en) 1998-11-10
EP0789869A4 (en) 2006-09-13
WO1996014616A1 (en) 1996-05-17
CA2203776C (en) 2002-10-08
CN1229482A (en) 1999-09-22
EP0789869A1 (en) 1997-08-20
NZ296732A (en) 1999-11-29

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