BRPI0517208A - método de redução de funções para máquinas de decisão - Google Patents

método de redução de funções para máquinas de decisão

Info

Publication number
BRPI0517208A
BRPI0517208A BRPI0517208-0A BRPI0517208A BRPI0517208A BR PI0517208 A BRPI0517208 A BR PI0517208A BR PI0517208 A BRPI0517208 A BR PI0517208A BR PI0517208 A BRPI0517208 A BR PI0517208A
Authority
BR
Brazil
Prior art keywords
functions
decision
vectors
support vector
reduction method
Prior art date
Application number
BRPI0517208-0A
Other languages
English (en)
Inventor
Kevin E Gates
Original Assignee
Univ Queensland
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
Priority claimed from AU2004907169A external-priority patent/AU2004907169A0/en
Application filed by Univ Queensland filed Critical Univ Queensland
Publication of BRPI0517208A publication Critical patent/BRPI0517208A/pt

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F15/00—Digital computers in general; Data processing equipment in general
    • G06F15/76—Architectures of general purpose stored program computers
    • G06F15/80—Architectures of general purpose stored program computers comprising an array of processing units with common control, e.g. single instruction multiple data processors
    • G06F15/8053—Vector processors
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/245—Classification techniques relating to the decision surface
    • G06F18/2451—Classification techniques relating to the decision surface linear, e.g. hyperplane
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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]

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Software Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Computing Systems (AREA)
  • Medical Informatics (AREA)
  • Mathematical Physics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Computer Hardware Design (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Feedback Control In General (AREA)
  • Testing And Monitoring For Control Systems (AREA)
  • Image Analysis (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

MéTODO DE REDUçãO DE FUNçõES PARA MáQUINAS DE DECISãO. Um método de redução de funções num conjunto de treinamento para uma máquina de aprendizado tal como uma Máquina de Vetor Suporte. Em uma concretização o método inclui o passo (35) de receber vetores de dados de treinamento entrados x~ i~, do conjunto de treinamento. Os vetores de dados de treinamento entrados são tipicamente derivados de um conjunto de funções num espaço de função. No passo (37), os vetores de dados entrados são mapeados num espaço multidimensional. No passo (39), um problema de mínimos quadrados, derivado da formulação da Máquina de Vetor Suporte, é resolvido para determinar quais funções compreendendo os vetores de treinamento devam ser consideradas significativas. No passo (41), os parâmetros de decisão e os vetores de uma máquina de decisão escolhida, uma Máquina de Vetor Suporte, por exemplo, são determinados utilizando as funções determinadas como significativas no passo (39).
BRPI0517208-0A 2004-12-17 2005-12-14 método de redução de funções para máquinas de decisão BRPI0517208A (pt)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
AU2004907169A AU2004907169A0 (en) 2004-12-17 Natural feature selection for support vector machines
AU2005903131A AU2005903131A0 (en) 2005-06-10 Feature reduction methods for classification systems
PCT/AU2005/001888 WO2006063395A1 (en) 2004-12-17 2005-12-14 Feature reduction method for decision machines

Publications (1)

Publication Number Publication Date
BRPI0517208A true BRPI0517208A (pt) 2008-09-30

Family

ID=36587453

Family Applications (1)

Application Number Title Priority Date Filing Date
BRPI0517208-0A BRPI0517208A (pt) 2004-12-17 2005-12-14 método de redução de funções para máquinas de decisão

Country Status (11)

Country Link
US (1) US7831527B2 (pt)
EP (1) EP1831795A4 (pt)
JP (1) JP2008524675A (pt)
KR (1) KR20070092727A (pt)
BR (1) BRPI0517208A (pt)
CA (1) CA2591402A1 (pt)
IL (1) IL183903A0 (pt)
MX (1) MX2007007247A (pt)
NO (1) NO20073362L (pt)
RU (1) RU2007123771A (pt)
WO (1) WO2006063395A1 (pt)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7979367B2 (en) * 2007-03-27 2011-07-12 Nec Laboratories America, Inc. Generalized sequential minimal optimization for SVM+ computations
US8463591B1 (en) * 2009-07-31 2013-06-11 Google Inc. Efficient polynomial mapping of data for use with linear support vector machines
JP5637373B2 (ja) * 2010-09-28 2014-12-10 株式会社Ｓｃｒｅｅｎホールディングス 画像の分類方法、外観検査方法および外観検査装置
US20140241618A1 (en) * 2013-02-28 2014-08-28 Hewlett-Packard Development Company, L.P. Combining Region Based Image Classifiers
CN103678514B (zh) * 2013-11-26 2017-01-18 科大讯飞股份有限公司 一种业务趋势预测方法及系统
EP3188093A1 (en) * 2015-12-29 2017-07-05 Tata Consultancy Services Limited System and method for predicting response time of an enterprise system

Family Cites Families (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7079955B2 (en) * 2000-11-03 2006-07-18 Procognia, Ltd. System and method for integrated analysis of data for characterizing carbohydrate polymers
US7415359B2 (en) * 2001-11-02 2008-08-19 Gene Network Sciences, Inc. Methods and systems for the identification of components of mammalian biochemical networks as targets for therapeutic agents
JP3889663B2 (ja) * 2002-05-13 2007-03-07 日本電信電話株式会社 分類装置、分類方法、分類プログラム及びそのプログラムを記録した記録媒体
US7174040B2 (en) * 2002-07-19 2007-02-06 Intel Corporation Fast method for training and evaluating support vector machines with a large set of linear features
US7478074B2 (en) * 2003-10-31 2009-01-13 The University Of Queensland Support vector machine
US7356187B2 (en) * 2004-04-12 2008-04-08 Clairvoyance Corporation Method and apparatus for adjusting the model threshold of a support vector machine for text classification and filtering
US7401012B1 (en) * 2005-04-20 2008-07-15 Sun Microsystems, Inc. Method and apparatus for characterizing computer system workloads
US7398191B1 (en) * 2005-04-20 2008-07-08 Sun Microsystems, Inc. Method and apparatus for computing a distance metric between computer system workloads
US7406450B2 (en) * 2005-09-28 2008-07-29 Nec Laboratories America, Inc. Spread kernel support vector machine

Also Published As

Publication number Publication date
US7831527B2 (en) 2010-11-09
IL183903A0 (en) 2007-10-31
MX2007007247A (es) 2007-09-04
KR20070092727A (ko) 2007-09-13
CA2591402A1 (en) 2006-06-22
US20090204553A1 (en) 2009-08-13
NO20073362L (no) 2007-06-29
JP2008524675A (ja) 2008-07-10
EP1831795A1 (en) 2007-09-12
EP1831795A4 (en) 2010-01-20
WO2006063395A1 (en) 2006-06-22
RU2007123771A (ru) 2009-01-27

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Legal Events

Date Code Title Description
B08F Application dismissed because of non-payment of annual fees [chapter 8.6 patent gazette]

Free format text: REFERENTE AS 5A E 6A ANUIDADES.

B08K Patent lapsed as no evidence of payment of the annual fee has been furnished to inpi [chapter 8.11 patent gazette]

Free format text: REFERENTE AO DESPACHO 8.6 PUBLICADO NA RPI 2160 DE 29/05/2012.