WO2003083760A3 - Methods and devices relating to estimating classifier performance - Google Patents

Methods and devices relating to estimating classifier performance Download PDF

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Publication number
WO2003083760A3
WO2003083760A3 PCT/US2003/009657 US0309657W WO03083760A3 WO 2003083760 A3 WO2003083760 A3 WO 2003083760A3 US 0309657 W US0309657 W US 0309657W WO 03083760 A3 WO03083760 A3 WO 03083760A3
Authority
WO
WIPO (PCT)
Prior art keywords
methods
devices
feature
classifier performance
estimating
Prior art date
Application number
PCT/US2003/009657
Other languages
French (fr)
Other versions
WO2003083760A2 (en
Inventor
Cole Coryell Harris
Original Assignee
Quasar International Inc
Cole Coryell Harris
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 Quasar International Inc, Cole Coryell Harris filed Critical Quasar International Inc
Priority to JP2003581108A priority Critical patent/JP2005524131A/en
Priority to AU2003220578A priority patent/AU2003220578A1/en
Priority to EP03716892A priority patent/EP1495444A4/en
Publication of WO2003083760A2 publication Critical patent/WO2003083760A2/en
Publication of WO2003083760A3 publication Critical patent/WO2003083760A3/en

Links

Classifications

    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/40ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00Pattern recognition
    • G06F18/20Analysing
    • G06F18/21Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/211Selection of the most significant subset of features
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00Pattern recognition
    • G06F18/20Analysing
    • G06F18/21Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/217Validation; Performance evaluation; Active pattern learning techniques
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00Pattern recognition
    • G06F18/20Analysing
    • G06F18/24Classification techniques
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Abstract

Methods and devices, including methods and devices for estimating classifier performance such as generalization performance, are disclosed. One method includes providing multiple samples (10). Each sample is characterized by one or more features. This method also includes associating a feature variability with at least one of the one or more features on a feature-by-feature basis (20); and computing a first probability of misclassification (30) by a first classifier using the feature variability. Devices, including integrated circuits (ICs) and field programmable gate arrays (FPGAs), that are configured for use in carrying out the present methods are also disclosed.
PCT/US2003/009657 2002-03-28 2003-03-28 Methods and devices relating to estimating classifier performance WO2003083760A2 (en)

Priority Applications (3)

Application Number Priority Date Filing Date Title
JP2003581108A JP2005524131A (en) 2002-03-28 2003-03-28 Method and apparatus for classifier performance estimation
AU2003220578A AU2003220578A1 (en) 2002-03-28 2003-03-28 Methods and devices relating to estimating classifier performance
EP03716892A EP1495444A4 (en) 2002-03-28 2003-03-28 Methods and devices relating to estimating classifier performance

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US10/109,094 2002-03-28
US10/109,094 US7561971B2 (en) 2002-03-28 2002-03-28 Methods and devices relating to estimating classifier performance

Publications (2)

Publication Number Publication Date
WO2003083760A2 WO2003083760A2 (en) 2003-10-09
WO2003083760A3 true WO2003083760A3 (en) 2003-12-04

Family

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Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2003/009657 WO2003083760A2 (en) 2002-03-28 2003-03-28 Methods and devices relating to estimating classifier performance

Country Status (5)

Country Link
US (3) US7561971B2 (en)
EP (1) EP1495444A4 (en)
JP (1) JP2005524131A (en)
AU (1) AU2003220578A1 (en)
WO (1) WO2003083760A2 (en)

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US7787969B2 (en) 2007-06-15 2010-08-31 Caterpillar Inc Virtual sensor system and method
US7831416B2 (en) 2007-07-17 2010-11-09 Caterpillar Inc Probabilistic modeling system for product design
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US8224468B2 (en) * 2007-11-02 2012-07-17 Caterpillar Inc. Calibration certificate for virtual sensor network (VSN)
US20140324677A1 (en) * 2008-05-19 2014-10-30 Jpmorgan Chase Bank, N.A. Method and system for detecting, monitoring and investigating first party fraud
US8086640B2 (en) 2008-05-30 2011-12-27 Caterpillar Inc. System and method for improving data coverage in modeling systems
US7917333B2 (en) 2008-08-20 2011-03-29 Caterpillar Inc. Virtual sensor network (VSN) based control system and method
US8897544B2 (en) 2009-12-10 2014-11-25 Indiana University Research And Technology Corp. System and method for segmentation of three-dimensional image data
EP2524337B1 (en) 2010-01-12 2022-10-26 Rigel Pharmaceuticals, Inc. Mode of action screening method
US8396875B2 (en) * 2010-06-17 2013-03-12 Microsoft Corporation Online stratified sampling for classifier evaluation
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US8873843B2 (en) * 2011-05-31 2014-10-28 Nec Laboratories America, Inc. Fast methods of learning distance metric for classification and retrieval
US8793004B2 (en) 2011-06-15 2014-07-29 Caterpillar Inc. Virtual sensor system and method for generating output parameters
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US10832130B2 (en) * 2016-09-19 2020-11-10 Google Llc Recommending a document for a user to access
RU2665244C1 (en) 2017-06-06 2018-08-28 Общество С Ограниченной Ответственностью "Яндекс" Metric generalized parameter forming for a/b testing methods and system
CN109218255B (en) * 2017-06-30 2021-06-04 中国电信股份有限公司 Safety protection method, control system and safety protection system
JP2021528643A (en) * 2018-06-22 2021-10-21 イーエヌデータクト ゲーエムベーハーiNDTact GmbH Sensor placement structure, use of sensor placement structure, and how to detect solid-borne sound
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Also Published As

Publication number Publication date
US20100198764A1 (en) 2010-08-05
US20090276384A1 (en) 2009-11-05
US7698071B2 (en) 2010-04-13
AU2003220578A1 (en) 2003-10-13
JP2005524131A (en) 2005-08-11
EP1495444A2 (en) 2005-01-12
WO2003083760A2 (en) 2003-10-09
US7561971B2 (en) 2009-07-14
EP1495444A4 (en) 2008-04-09
US20030187584A1 (en) 2003-10-02
AU2003220578A8 (en) 2003-10-13

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