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IL168091A - Generic classification system - Google Patents

Generic classification system

Info

Publication number
IL168091A
IL168091A IL168091A IL16809105A IL168091A IL 168091 A IL168091 A IL 168091A IL 168091 A IL168091 A IL 168091A IL 16809105 A IL16809105 A IL 16809105A IL 168091 A IL168091 A IL 168091A
Authority
IL
Israel
Prior art keywords
classification
training
classification system
algorithm
vectors
Prior art date
Application number
IL168091A
Original Assignee
Rafael Advanced Defense Sys
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 Rafael Advanced Defense Sys filed Critical Rafael Advanced Defense Sys
Priority to IL168091A priority Critical patent/IL168091A/en
Priority to US11/911,722 priority patent/US20100049674A1/en
Priority to PCT/IL2006/000470 priority patent/WO2006111963A2/en
Priority to EP06728271A priority patent/EP1872189A4/en
Publication of IL168091A publication Critical patent/IL168091A/en

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q90/00—Systems or methods specially adapted for administrative, commercial, financial, managerial or supervisory purposes, not involving significant data processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
    • 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
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Software Systems (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Medical Informatics (AREA)
  • Data Mining & Analysis (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Mathematical Physics (AREA)
  • Economics (AREA)
  • General Health & Medical Sciences (AREA)
  • Databases & Information Systems (AREA)
  • Health & Medical Sciences (AREA)
  • Multimedia (AREA)
  • General Business, Economics & Management (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Image Analysis (AREA)

Claims (20)

WHAT IS CLAIMED IS:
1. A classification system, comprising: (a) a training device for: (i) selecting which one of a plurality of training classification algorithms best classifies a set of training vectors, and (ii) finding a set of values, of parameters of a generic classification algorithm, that enable said generic classification algorithm to substantially emulate said selected training classification algorithm; and (b) at least one classification device for classifying at least one vector other than said training vectors, using said generic classification algorithm with said values.
2. The classification system of claim 1, wherein each said at least one classification device is revesibly operationally connectable to said training device.
3. The classification system of claim 1, wherein said training vectors sample a feature space, and wherein said finding is effected by steps including: (A) resampling said feature space, thereby obtaining a set of resampling vectors; and (B) classifying said resampling vectors using said training classification algorithm that best classifies said set of training vectors.
4. The classification system of claim 3, wherein said resampling resamples said feature space more densely than said feature space is sampled by said training vectors.
5. The classification system of claim 1 , comprising a plurality of said classification devices.
6. The classification system of claim 1, wherein said training device is further operative to dimensionally reduce said set of training vectors prior to said selecting of said training classification algorithm that best classifies said set of training vectors, and wherein each said at least one classification device is further operative to dimensionally reduce said at least one other vector in a like manner prior to said classifying of said at least one other vector.
7. The classification system of claim 1, further comprising: (c) for each said classification device, a respective memory, for storing said values, that is reversibly operationally connectable to said training device and to said each classification device.
8. The classification system of claim 1, wherein each said classification device includes a mechanism for executing said generic classification algorithm.
9. The classification system of claim 8, wherein said mechanism includes a general purpose processor.
10. The classification system of claim 8, wherein said mechanism includes a nonvolatile memory for storing program code of said generic classification algorithm.
11. 1 1. The classification system of claim 8, wherein said mechanism includes a field programmable gate array.
12. The classification system of claim 8, wherein said mechanism includes an application-specific integrated circuit.
13. The classification system of claim 1 , wherein said generic classification algorithm is a k-nearest-neighbors algorithm.
14. The classification system of claim 1 , wherein said training device includes a nonvolatile memory for storing program code for effecting said selecting and said finding.
15. The classification system of claim 14, wherein at least a portion of said program code is included in a dynamically linked library.
16. A classification system, comprising: (a) a training device for selecting which one of a plurality of classification algorithms best classifies a set of training vectors; and (b) at least one classification device for classifying at least one vector other than said training vectors, using said selected classification algorithm.
17. The classification system of claim 16, wherein each said at least one classification device includes: (i) a mechanism for executing said classification algorithms; and (ii) a memory for storing an indication of which one of said classification algorithms has been selected by said training device.
18. The classification system of claim 17, wherein said memory is also for storing at least one parameter of said classification algorithm that has been selected by said training device.
19. The classification system of claim 16, wherein each said at least one classification device includes a mechanism for executing said classification algorithms, and wherein the classification system further comprises: (c) for each said classification device, a respective memory, for storing an indication of which one of said classification algorithms has been selected by said training device, that is reversibly operationally connectable to said training device and to said each classification device.
20. The classification system of claim 19, wherein said respective memory is also for storing at least one parameter of said classification algorithm that has been selected by said training device. Advocate, Patent Attorney Moshe Aviv Tower 54th floor 7 Jabotinsky 52520 Ramat Gan
IL168091A 2005-04-17 2005-04-17 Generic classification system IL168091A (en)

Priority Applications (4)

Application Number Priority Date Filing Date Title
IL168091A IL168091A (en) 2005-04-17 2005-04-17 Generic classification system
US11/911,722 US20100049674A1 (en) 2005-04-17 2006-04-11 Generic classification system
PCT/IL2006/000470 WO2006111963A2 (en) 2005-04-17 2006-04-11 Generic classification system
EP06728271A EP1872189A4 (en) 2005-04-17 2006-04-11 Generic classification system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
IL168091A IL168091A (en) 2005-04-17 2005-04-17 Generic classification system

Publications (1)

Publication Number Publication Date
IL168091A true IL168091A (en) 2010-04-15

Family

ID=37115560

Family Applications (1)

Application Number Title Priority Date Filing Date
IL168091A IL168091A (en) 2005-04-17 2005-04-17 Generic classification system

Country Status (4)

Country Link
US (1) US20100049674A1 (en)
EP (1) EP1872189A4 (en)
IL (1) IL168091A (en)
WO (1) WO2006111963A2 (en)

Families Citing this family (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7088872B1 (en) * 2002-02-14 2006-08-08 Cogent Systems, Inc. Method and apparatus for two dimensional image processing
US8131477B2 (en) * 2005-11-16 2012-03-06 3M Cogent, Inc. Method and device for image-based biological data quantification
US8275179B2 (en) * 2007-05-01 2012-09-25 3M Cogent, Inc. Apparatus for capturing a high quality image of a moist finger
US8411916B2 (en) * 2007-06-11 2013-04-02 3M Cogent, Inc. Bio-reader device with ticket identification
US20100014755A1 (en) * 2008-07-21 2010-01-21 Charles Lee Wilson System and method for grid-based image segmentation and matching
US10679749B2 (en) * 2008-08-22 2020-06-09 International Business Machines Corporation System and method for virtual world biometric analytics through the use of a multimodal biometric analytic wallet
WO2012127577A1 (en) * 2011-03-18 2012-09-27 富士通フロンテック株式会社 Verification device, verification program, and verification method
CN108737379A (en) * 2018-04-19 2018-11-02 河海大学 A kind of big data transmission process algorithm
CN109145554A (en) * 2018-07-12 2019-01-04 温州大学苍南研究院 A kind of recognition methods of keystroke characteristic abnormal user and system based on support vector machines

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5142593A (en) * 1986-06-16 1992-08-25 Kabushiki Kaisha Toshiba Apparatus and method for classifying feature data at a high speed
US6678548B1 (en) * 2000-10-20 2004-01-13 The Trustees Of The University Of Pennsylvania Unified probabilistic framework for predicting and detecting seizure onsets in the brain and multitherapeutic device
US20020165839A1 (en) * 2001-03-14 2002-11-07 Taylor Kevin M. Segmentation and construction of segmentation classifiers
US6879709B2 (en) * 2002-01-17 2005-04-12 International Business Machines Corporation System and method for automatically detecting neutral expressionless faces in digital images
US6938049B2 (en) * 2002-06-11 2005-08-30 The Regents Of The University Of California Creating ensembles of decision trees through sampling
US7146050B2 (en) * 2002-07-19 2006-12-05 Intel Corporation Facial classification of static images using support vector machines
US7073013B2 (en) * 2003-07-03 2006-07-04 H-Systems Flash Disk Pioneers Ltd. Mass storage device with boot code
US7319779B1 (en) * 2003-12-08 2008-01-15 Videomining Corporation Classification of humans into multiple age categories from digital images

Also Published As

Publication number Publication date
WO2006111963A3 (en) 2007-05-31
EP1872189A4 (en) 2010-03-03
EP1872189A2 (en) 2008-01-02
US20100049674A1 (en) 2010-02-25
WO2006111963A2 (en) 2006-10-26

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