• 79 Pages DHSChap3
    DHSChap3

    School: Neumont

    Contents 3 Maximum likelihood and Bayesian estimation 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Maximum Likelihood Estimation . . . . . . . . . . . . . . . . . . 3.2.1 The General Principle . . . . . . . . . . .

  • 528 Pages t06MethodesParametriques
    T06MethodesParametriques

    School: Neumont

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  • 375 Pages t01Introduction_2
    T01Introduction_2

    School: Neumont

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  • 208 Pages l01_ProbabilityRoweis
    L01_ProbabilityRoweis

    School: Neumont

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  • 6 Pages l07_MarginMaximizingRatsch
    L07_MarginMaximizingRatsch

    School: Neumont

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  • 9 Pages sebasRapport
    SebasRapport

    School: Neumont

    Rduction de dimensionalit: analyse de l'algorithme Isomap, et application en bioinformatique gnomique. Sbastien Christin Laboratoire de Biologie Informatique Thorique Universit de Montral Montral, QC, CA christis@iro.umontreal.ca Abstract On en

  • 8 Pages manzagolRapport
    ManzagolRapport

    School: Neumont

    IFT6390 - Rapport de la pr sentation e ` Introduction a lapprentissage semi-supervis e Pierre-Antoine Manzagol manzagop[at]iro[dot]umontreal[dot]ca Abstract Dans le cadre du cours IFT6390, il faut r aliser une pr sentation sur un e e sujet avanc da

  • 1 Page d4
    D4

    School: Neumont

    IFT 3390/6390 H06 Fondements d'apprentissage machine Bal zs K gl a e Devoir 4 ` ` a remettre au plus tard le jeudi 13 avril a 10:30 1. A DA B OOST minimise le risque Re ( f (T ) ) = ou Le est la perte exponentielle p nalis e e e Le (a) Montrez que

  • 820 Pages t15ApprentissageNonSupervise2_2
    T15ApprentissageNonSupervise2_2

    School: Neumont

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  • 3350 Pages t04MethodesABaseDeVoisinageEncore_6
    T04MethodesABaseDeVoisinageEncore_6

    School: Neumont

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  • 12 Pages l01_ProbabilityRoweis
    L01_ProbabilityRoweis

    School: Neumont

    U u # P P ( C 5 " 4 Q 4 Q $ $ Q v 5 5 I 3 P' d ab c R G HF r 0I c f g h2 P I I 3 3 ' S ' 4 B$ 4 ') 3( I 3 P ( R GHF 5' Q d e ') P 2 ' D ( 4 B S ' 6 PX 3 ( S B$ 5 3 9 %D 3 69 ' D p T R G HF w xI y G F v R R t HF 3 ' T ' p qQ ' P' R t HF '

  • 22 Pages tong
    Tong

    School: Neumont

    Journal of Machine Learning Research (2001) 45-66 Submitted 10/01; Published 11/01 Support Vector Machine Active Learning with Applications to Text Classification Simon Tong Daphne Koller Computer Science Department Stanford University Stanford CA

  • 13 Pages multiclass2
    Multiclass2

    School: Neumont

    ) 'o x r v 0}ci2sm u v l m r x x o o l o 'o x p & $ " p x u z u v m u pc1nocxnxpioh"nr(' o o'm #1f%#sh !s1"{oi'}2iowxcpwyznmcsilwx baaT e d baT rV a q W a r d a W br qVT b WdY T d d a Wd b r WV aT c"'Xsc"U"}ca2YUU"hX

  • 5 Pages t15ApprentissageNonSupervise2_6
    T15ApprentissageNonSupervise2_6

    School: Neumont

    Apprentissage non-supervise Typologie de la reduction de dimension methode de base: ACP "groupement (clustering) des dimensions" extensions: ACP non-lineaire (NLPCA) echelonnement multidimensionnel (multidimensional scaling MDS) cartes a

  • 679 Pages t09ArbresDeDecision_2
    T09ArbresDeDecision_2

    School: Neumont

    %!PS-Adobe-2.0 %Creator: dvips(k) 5.95a Copyright 2005 Radical Eye Software %Title: t09ArbresDeDecision.dvi %Pages: 8 0 %PageOrder: Ascend %BoundingBox: 0 0 612 459 %DocumentFonts: Times-Roman Helvetica CMSY10 Symbol Times-Italic CMR10 %+ CMEX10 CMMI

  • 4 Pages lle
    Lle

    School: Neumont

    REPORTS 35. R. N. Shepard, Psychon. Bull. Rev. 1, 2 (1994). 36. J. B. Tenenbaum, Adv. Neural Info. Proc. Syst. 10, 682 (1998). 37. T. Martinetz, K. Schulten, Neural Netw. 7, 507 (1994). 38. V. Kumar, A. Grama, A. Gupta, G. Karypis, Introduction to Pa

  • 4 Pages l03_PerceptronKaplan
    L03_PerceptronKaplan

    School: Neumont

    Perception without Awareness, Psychology of unconscious from conscious cognition. Journal of Experimental Psychology: General 124: 2242 Greenwald A G, Spangenberg E R, Pratkanis A R, Eskanazi J 1991 Double-blind tests of subliminal self-help audiotap

  • 270 Pages l07_IntroBoostingFreundSchapire
    L07_IntroBoostingFreundSchapire

    School: Neumont

    %!PS-Adobe-2.0 %Creator: dvipsk 5.58f Copyright 1986, 1994 Radical Eye Software %Title: paper.dvi %Pages: 14 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %DocumentFonts: Times-Roman Times-Bold Times-Italic %EndComments %DVIPSCommandLine: dvips -D600

  • 50 Pages DHSChapX
    DHSChapX

    School: Neumont

    Contents 6 Multilayer Neural Networks 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Feedforward operation and classification . . . . . . . . . . 6.2.1 General feedforward operation . . . . . . . . . . . 6.2.2 Expressive pow

  • 91 Pages DHSChap10
    DHSChap10

    School: Neumont

    Contents 10 Unsupervised Learning and Clustering 10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . 10.2 Mixture Densities and Identiability . . . . . . . . . 10.3 Maximum-Likelihood Estimates . . . . . . . . . . . . 10.4 Application to

  • 49 Pages DHSChap7
    DHSChap7

    School: Neumont

    Contents 7 Stochastic Methods 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Stochastic search . . . . . . . . . . . . . . . . . . . . . 7.2.1 Simulated annealing . . . . . . . . . . . . . . . 7.2.2 The Boltzmann factor . . . .

  • 72 Pages DHSChap5
    DHSChap5

    School: Neumont

    Contents 5 Linear Discriminant Functions 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Linear Discriminant Functions and Decision Surfaces . . . . . . . . . 5.2.1 The Two-Category Case . . . . . . . . . . . . .

  • 24 Pages DHSChap1
    DHSChap1

    School: Neumont

    Contents 1 Introduction 1.1 Machine Perception . . . . . . . . . . . . . 1.2 An Example . . . . . . . . . . . . . . . . . . 1.2.1 Related fields . . . . . . . . . . . . . 1.3 The Sub-problems of Pattern Classification 1.3.1 Feature Extraction . . . .

  • 17 Pages l07_MarginMaximizingRatsch
    L07_MarginMaximizingRatsch

    School: Neumont

    Efcient Margin Maximizing with Boosting Gunnar R tsch a The Australian National University Canberra, ACT 0200, Australia G UNNAR .R AETSCH @ ANU . EDU . AU Manfred K. Warmuth University of California at Santa Cruz Santa Cruz, CA 95060, USA MANFRED

  • 8 Pages l08_TextCategorizationCaiHofmann
    L08_TextCategorizationCaiHofmann

    School: Neumont

    Text Categorization by Boosting Automatically Extracted Concepts Department of Computer Science Brown University, Providence, RI, USA Lijuan Cai Department of Computer Science Brown University, Providence, RI, USA Thomas Hofmann th@cs.brown.edu l

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