Software by Kevin Murphy and students

Software by Kevin Murphy and students:



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decision boundary

knnexample:

http://www.dcs.gla.ac.uk/~srogers/firstcourseml/matlab/chapter5/knnexample.html

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Decision Trees and Predictive Models with cross-validation and ROC analysis plot - File Exchange - MATLAB Central

Decision Trees and Predictive Models with cross-validation and ROC analysis plot - File Exchange - MATLAB Central:



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http://www.mathworks.com/matlabcentral/fileexchange/26326-decision-trees-and-predictive-models-with-cross-validation-and-roc-analysis-plot

Machine Learning/Decision Trees/C4.5 Example 1

Machine Learning/Decision Trees/C4.5 Example 1:

http://www2.cs.uregina.ca/~dbd/cs831/notes/ml/dtrees/c4.5/c4.5_prob1.html



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machine xls

Recursive Average & Variance Tutorial:



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http://people.revoledu.com/kardi/tutorial/RecursiveStatistic/index.html

% POWER SPECTRUM ANALYSIS WITH MATLAB FFT

"
% POWER SPECTRUM ANALYSIS WITH MATLAB FFT
"



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fft good

Index of /~robvdw/DGCN22/PRACTICUM_2011/MATLAB_FFT: "Index of /~robvdw/DGCN22/PRACTICUM_2011/MATLAB_FFT

Parent Directory
GVH_0_complex_rekenen.pdf
He"

Index of /~robvdw/DGCN22/PRACTICUM_2011/MATLAB_FFT

• Parent Directory
• GVH_0_complex_rekenen.pdf
• He

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MSek Signal Analysis Home Page

MSek Signal Analysis Home Page:

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What is Mahalanobis distance? - The DO Loop

What is Mahalanobis distance? - The DO Loop:

'via Blog this'SAS BLOGS HOME > THE DO LOOP > WHAT IS MAHALANOBIS DISTANCE?

What is Mahalanobis distance?

Rick Wicklin|FEBRUARY 15, 2012

• 65555
• 51

I previously described how to use Mahalanobis distance to find outliers in multivariate data. This article takes a closer look at Mahalanobis distance. A subsequent article will describe how you can compute Mahalanobis distance.

Distance in standard units

In statistics, we sometimes measure "nearness" or "farness" in terms of the scale of the data. Often "scale" means "standard deviation." For univariate data, we say that an observation that is one standard deviation from the mean is closer to the mean than an observation that is three standard deviations away. (You can also specify the distance between two observations by specifying h

CS340 (Machine learning) Fall 2006

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Lectures MWF 4-5, Dempster 301
Instructor: Kevin Murphy
Newsgroup

NEWS UPDATE

• 28 Nov: hw9 cancelled
• 21 Nov: I have added Code.zip which contains all the code in the handouts (and lots more!)
• 9 Nov: here is a list of interesting classes for Spring 07
  • Stat406, algorithms for classification and prediction, Murph
  • CS406, Optimization, Friedlander
  • CS322, AI, Leyton-Brown
  • CS420, advanced algorithms, Evans
• 3 Nov: Hao's discussion section has been replaced by office hours on Fridays, 2-3, X568.

CTAN: tex-archive/macros/latex/contrib/elsarticle

CTAN: tex-archive/macros/latex/contrib/elsarticle:

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FastICA

FastICA:

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• Aalto.fi 
• sci.aalto.fi 
• ics.aalto.fi 
• Independent Component Analysis (ICA) and Blind Source Separation (BSS)

Independent Component Analysis (ICA) and Blind Source Separation (BSS)

Jason J. Corso

Jason J. Corso:

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CSE 455/555 Introduction to Pattern Recognition
SUNY at Buffalo
Webpage for Spring 2013

Instructor: Jason Corso (UBIT: jcorso)

Course Webpage: http://www.cse.buffalo.edu/~jcorso/t/CSE555 or 
http://www.cse.buffalo.edu/~jcorso/t/CSE455 but this is just a link to the first one.

▶ Support Vector and Kernel Methods - VideoLectures.NET

▶ Support Vector and Kernel Methods - VideoLectures.NET:

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Support Vector and Kernel Methods

author: John Shawe-Taylor, Centre for Computational Statistics and Machine Learning, University College London
published: Feb. 25, 2007,   recorded: July 2005,   views: 2155

Categories

• Top » Computer Science » Machine Learning » Kernel Methods
• Top » Computer Science » Machine Learning » Kernel Methods » Support Vector Machines

Andrew Zisserman, University of Oxford

Andrew Zisserman, University of Oxford: "C19 Machine Learning lectures Hilary 2013

Lecture 1 "Introduction"
Lecture 2 "The SVM Classifier"
Lecture 3 "Dual problems and kernels"
Lecture 4 "More classifiers and optimization" "

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SVM Application List:

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SVM Application List

This list of Support Vector Machine applications grows thanks to visitors like you who ADD new entries. Thank you in advance for your contribution. 

Support vector machines-based generalized predictive control

This work presents an application of the previously proposed Support Vector Machines Based Generalized Predictive Control (SVM-Based GPC) method to the problem of controlling chaotic dynamics with small parameter perturbations. The Generalized Predictive Control (GPC) method, which is included in the

makebib.perl Gateway

makebib.perl Gateway:

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Giovanni Montana

Giovanni Montana: "Online Support Vector Regression
"

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Online Support Vector Regression

ONLINE SUPPORT VECTOR REGRESSION

ONLINE SUPPORT VECTOR REGRESSION
vhttp://onlinesvr.altervista.org/

Vibration Monitoring Analysis | Machine Vibration | Vibration Program

Vibration Monitoring Analysis | Machine Vibration | Vibration Program: "Vibration Analysis Reference

Here you will find handy (and free!) vibration analysis references and utilities from Azima DLI.  Check back from time to time for new items."

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Vibration Analysis Reference

Here you will find handy (and free!) vibration analysis references and utilities from Azima DLI.  Check back from time to time for new items.

Bearing Analysis Web Workshop #1: Detecting and Reducing Bearing Defects

Bearing Analysis Web Workshop #1: Detecting and Reducing Bearing Defects:

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Bearing Analysis Web Workshop #1: Detecting and Reducing Bearing Defects by Jason Tranter of MobiusPlease have patience while the presentation files load