Tuesday, December 31, 2013

Understanding the Bias-Variance Tradeoff

Understanding the Bias-Variance Tradeoff:

'via Blog this'

Sunday, December 29, 2013

svm - Matlab libsvm - how to find the w coefficients - Stack Overflow

svm - Matlab libsvm - how to find the w coefficients - Stack Overflow: "bias = -model.rho;"

'via Blog thi

odel = svmtrain(...);
w = (model.sv_coef' * full(model.SVs));

And the bias is (and I don't really remember why its negative):

bias = -model.rho;

Research Writing - Kittipat's Homepage

Research Writing - Kittipat's Homepage:

'via Blog this'

Research Writing

In this page, I collect some sentences and usages in order to learn their patterns to use in my writing. Moreover, I intend to remark some mistakes and confusions I often made when writing with the hope that they will be useful for others too.

Subscribe to posts

Adverb in front of a sentence

posted Sep 28, 2011, 11:15 AM by Kittipat Kampa

In essence; By nature; Essentially.

When describing corresponding items in order

posted Sep 9, 2011, 12:46 PM by Kittipat Kampa [ updated Sep 9, 2011, 12:58 PM ]

The following senten

Classification toolbox for MATLAB

Classification toolbox for MATLAB:

'via Blog this'Example

The Sediment dataset

Working with the graphical interface

Working with the command line

The Sediment dataset

The Sediment dataset is composed by 1884 sediment samples. Samples were divided into 2 classes on the basis of their toxicity (class 1: non toxic, class 2: toxic) and described by 9 chemical variables. The dataset was randomly divided in two sets, one training set (1413 samples) and one test set (471 samples). The dataset has been published in the following papers:

Saturday, December 28, 2013

Tinnitus a Gift from God

Tinnitus a Gift from God:

Visualize classifier decision boundaries in MATLAB

WHEN I needed to plot classifier decision boundaries for my thesis, I decided to do it as simply as possible. Although the decision boundaries between classes can be derived analytically, plotting them for more than two classes gets a bit complicated. You have to find the intersection of regions that are all assigned to a particular class and then find the expression for the boundary of that class. If analytical boundaries are not necessary, then a brute force, computational approach can be used. This tutorial does such an approach: the feature space is divided up into a grid and then each grid cell is classified. The classified map is then shown as an image behind a scatter plot of the training data. This is an application of how to plot over an image background in MATLAB. The result will look something like this (for a city block classifier):

Create Data

Introduction to one-class Support Vector Machines - Roemer's blog

Introduction to one-class Support Vector Machines - Roemer's blog: "a"

'via Blog this'

boundary arises.

The hyperplane is

Xu Cui

SVM regression with libsvm

May 20th, 2010

98 comments

SVM is mostly commonly used for binary classifications. But one branch of SVM, SVM regression or SVR, is able to fit a continuous function to data. This is particularly useful when the predicted variable is continuous. Here I tried some very simple cases using libsvm matlab package:

1. Feature 1D, use 1st half to train, 2nd half to test. The fitting is pretty good.

EE4389/8591: Support Vector Machine Regression

EE4389/8591: Support Vector Machine Regression:
http://www.ece.umn.edu/users/cherkass/ee4389/SVR.html

'via Blog this'

Plot the model
X = 0:0.01:1;
X = X';
y = ones(length(X), 1);
y_est = svmpredict(y, X, model);

h = plot(trn_data.X, trn_data.y, 'ko', tst_data.X, tst_data.y, 'kx', X, y_est, 'r--');

legend('Training', 'Test', 'Model');
y1 = max([trn_data.y; tst_data.y]);
y2 = min([trn_data.y; tst_data.y]);
axis([0 1 y2 y1]);

LibSVM scale-svm file in matlab - growling wolf running away ...... - blog channel - CSDN.NET

LibSVM scale-svm file in matlab - growling wolf running away ...... - blog channel - CSDN.NET:

'via Blog this'

LibSVM scale-svm file in matlab

Category: matlab2012-12-06 14:00 533 Read Comments (0) Collection Report

The data need to be normalized for furtuer using, Therefore you should do it at first, you can do this by using the libsvm svm-scale code or write a normalize file.
In addition, you can also mex the svm-scale.c, unfortunately, I can not get the right results.
If you are on windows, you should go by cmd order to run the svm-scale.exe to normalize your data

Thursday, December 26, 2013

Apparatus & Procedures

As shown in Figure 1 above, the test stand consists of a 2 hp motor (left), a torque transducer/encoder (center), a dynamometer (right), and control electronics (not shown). The test bearings support the motor shaft. Single point faults were introduced to the test bearings using electro-discharge machining with fault diameters of 7 mils, 14 mils, 21 mils, 28 mils, and 40 mils (1 mil=0.001 inches). See FAULT SPECIFICATIONS for fault depths. SKF bearings were used for the 7, 14 and 21 mils diameter faults, and NTN equivalent bearings were used for the 28 mil and 40 mil faults. Drive end and fan end bearing specifications, including bearing geometry and defect frequencies are listed in the BEARING SPECIFICATIONS.

Vibration data was collect

Tuesday, December 24, 2013

Welcome to Inkscape Community!

Welcome to Inkscape Community!:

'via Blog this'

Waluyo Adi Siswanto: LaTeX Formula in Inkscape Windows 7

Waluyo Adi Siswanto: LaTeX Formula in Inkscape Windows 7: "SATURDAY, NOVEMBER 3, 2012
LaTeX Formula in Inkscape Windows 7
The following steps will guide you to get LaTeX formula working in Inkscape running Windows7 (64 bits).

Step 1 - Install ghostscript
The ghostscript installer gs905w32.exe downloaded from here.

Step 2 - Install ghostview
The ghostview installer gsv50w32.exe downloaded from here.

Step 3 - Install ps"
'via Blog this'

Saturday, December 21, 2013

Tools for Machine Learning Performance Evaluation: Confusion Matrix - Artificial Intelligence in Motion

Tools for Machine Learning Performance Evaluation: Confusion Matrix - Artificial Intelligence in Motion:

'via Blog this'

Tools for Machine Learning Performance Evaluation: Confusion Matrix

TUESDAY, AUGUST 31, 2010

Hi all,

I'll start to write some posts starting from now about Supervised and Unsupervised learning, specific related to performance evaluation such as classification accuracy, lift, roc curves, F1-Score and errors.

The Confusion Matrix

Friday, December 20, 2013

Structural Risk Minimization

Structural Risk Minimization:

'via Blog this'

BENNETT, K. and A. DEMIRIZ, 1999. Semi-Supervised Support Vector Machines. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS. [Cited by 213] (23.11/year)
BENNETT, K.P. and J.A. BLUE, 1998. A support vector machine approach to decision trees. Neural Networks Proceedings, 1998. IEEE World Congress on …. [Cited by 59] (5.77/year)
BLANZ, V., et al., 1996. Comparison of view {based object recognition algorithms using realistic 3D models. Artificial Neural Networks: ICANN 96: 1996 International …. [Cited by 119] (9.74/year)
BURBIDGE, R., et al., 2001. Drug design by machine learning: support vector machines for pharmaceutical data analysis. Computers and Chemistry. [Cited by 197] (27.30/year)
BURGES, C.J.C., 1998. A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery. [Cited by 4678] (457.86/year)
CAMPS-VALLS, G. and L. BRUZZONE, 2005. Kernel-based methods for hyperspectral image classification. Geoscience and Remote Sensing, IEEE Transactions on. [Cited by 51] (15.85/year)
CHEN, S., A.K. SAMINGAN and L. HANZO, 2001. Support vector machine multiuser receiver for DS-CDMA signals inmultipath channels. Neural Networks, IEEE Transactions on. [Cited by 92] (12.75/year)
CLARKSON, P. and P.J. MORENO, 1999. On the use of support vector machines for phonetic classification. Acoustics, Speech, and Signal Processing, 1999. ICASSP'99. …. [Cited by 84] (9.11/year)
CORTES, C. and V. VAPNIK, 1995. Support-vector networks. Machine Learning. [Cited by 3688] (279.03/year)
CRISTIANINI, N., C. CAMPBELL and J. SHAWE-TAYLOR, 1999. Dynamically Adapting Kernels in Support Vector Machines. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS. [Cited by 94] (10.20/year)
D?NIZ, O., M. CASTRILL?N and M. HERN?NDEZ, 2003. Face recognition using independent component analysis and support vector machines. Pattern Recognition Letters. [Cited by 64] (12.27/year)
DING, A.L., X.M. ZHAO and L.C. JIAO, 2002. Traffic flow time series prediction based on statistics learning theory. Intelligent Transportation Systems, 2002. Proceedings. The …. [Cited by 13] (2.09/year)
DUAN, K., S.S. KEERTHI and A.N. POO, 2003. Evaluation of simple performance measures for tuning SVM hyperparameters. Neurocomputing. [Cited by 140] (26.83/year)
EL-NAQA, I., et al., 2002. Support vector machine learning for detection of microcalcifications in mammograms. Biomedical Imaging, 2002. Proceedings. 2002 IEEE …. [Cited by 20] (3.22/year)
EL-NAQA, I., et al., 2002. A Support Vector Machine Approach for Detection of Microcalcifications. IEEE TRANSACTIONS ON MEDICAL IMAGING. [Cited by 95] (15.28/year)
EVGENIOU, T., et al., 2002. Regularization and statistical learning theory for data analysis. Computational Statistics and Data Analysis. [Cited by 20] (3.22/year)
EVGENIOU, T., M. PONTIL and T. POGGIO, 2000. Statistical Learning Theory: A Primer. International Journal of Computer Vision. [Cited by 22] (2.68/year)
EVGENIOU, T., M. PONTIL and T. POGGIO, 2000. Re

Support Vector Machines

Support Vector Machines:

'via Blog this'

Advantages
Comparison with Artificial Neural Networks
Bagging
Bibliographies
Books
Classification
Comparisons with other techniques
Convexity
Disadvantages
Domain Knowledge
Duality
Feature Selection

Financial Applications
Glossary
Gram Matrix
Heuristics
Hilbert Space
History
Optimal Separating Hyperplanes
Implementation
Introduction
Journals
Kernels
Key Features

Karush-Kuhn-Tucker conditions
Lagrangian
Learnability
Links
Local
Mailing Lists
Mercer's Condition
Model Selection
Newsgroups
Norms
nu
PAC Learning

Parameters
People
Performance
Quadratic Programming
Regression
Regularization
Statistical Learning Theory
Software
Structural Risk Minimization
Survey
Top 100 Publications
Training
Tutorials
VC Dimension

Research - Michael J. Prerau

Research - Michael J. Prerau:

'via Blog this'

As a member of the Intrusion Detection Systems group at Columbia, I performed research on the use of machine learning as a way to detect attacks on computer networks. Specifically, I worked on using performing unsupervised anomaly detection (UAD) on large sets of network connection data. UAD attempts to find anomalous data instances within a sample space without knowing the labels of the preexisting instances.

Anomaly Detection and Machine Learning

ROC Curves

Carleton comps paper template - LaTeX Template on writeLaTeX

Carleton comps paper template - LaTeX Template on writeLaTeX:

'via Blog this'

Carleton comps paper template

Andrew Gainer-Dewar

Download PDF

Open as Template

This is a template for writing Carleton comps papers. It should be used in conjunction with the Carleton LaTeX workshop materials.

Find More Templates

Thursday, December 19, 2013

Support Vector Machines (SVM)

Support Vector Machines (SVM):

'via Blog this'

Creators of STATISTICA Data Analysis Software and Services

Search the Electronic Statistics Textbook

Support Vector Machines (SVM)

Support Vector Machines (SVM) Introductory Overview

Support Vector Machines are based on the concept of decision planes that define decision boundaries. A decision plane is one that separates between a set of objects having different class memberships. A schematic example is shown in the illustration below. In this example, the objects belong either to class GREEN or RED. The separating line defines a boundary on the right side of which all objects are GREEN and to the left of which all objects are RED. Any new object (white circle) falling to the right is labeled, i.e., classified, as GREEN (or classified as RED should it fall to the left of the separating line).

SVM - Support Vector Machines

SVM - Support Vector Machines:

'via Blog this'

Home

Support Vector Machines

Machine Learning

SVM - Support Vector Machines

Books

Review: Applications of Support Vector Machines in Chemistry, Rev. Comput. Chem. 2007, 23, 291-400

V. Vapnik and A. Chervonenkis, Theory of Pattern Recognition, Nauka, Moscow, 1974.
V. Vapnik, Estimation of Dependencies Based on Empirical Data, Nauka, Moscow, 1979.
V. Vapnik, The Nature of Statistical Learning Theory, Springer, 1995.
V. Vapnik, Statistical Learning Theory, Wiley-Interscience, New York, 1998.
B. Schölkopf, C. J. C. Burges, and A. J. Smola, Advances in Kernel Methods: Support Vector Learning, MIT Press, Cambridge, MA, 1999.
N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge University Press, Cambridge, 2000.
A. J. Smola, P. Bartlett, B. Schölkopf, and D. Schuurmans, Advances in Large Margin Classifiers, MIT Press, Cambridge, MA, 2000.
V. Kecman, Learning and Soft Computing, MIT Press, Cambridge, MA, 2001.
B. Schölkopf and A. J. Smola, Learning with Kernels, MIT Press, Cambridge, MA, 2002.
T. Joachims, Learning to Classify Text Using Support Vector Machines: Methods, Theory, and Algorithms, Kluwer, 2002.
R. Herbrich, Learning Kernel Classifiers, MIT Press, Cambridge, MA, 2002.
J. A. K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, and J. Vandewalle, Least Squares Support Vector Machines, World Scientific, Singapore, 2002.
J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis, Cambridge University Press, Cambridge, 2004.

asi.insa-rouen.fr/~arakotom/toolbox/ - Similar Sites and Reviews | Xmarks

asi.insa-rouen.fr/~arakotom/toolbox/ - Similar Sites and Reviews | Xmarks:

'via Blog this'

Share This Page Site: asi.insa-rouen.fr/~arakotom/toolbox/
SVM and Kernel Methods Matlab toolbox [History] [Report This] Bookmarks No reviews yet. Be the first. Ranked: #9 in Support Vector Machines, #129 in Machine Learning, #178 in Toolbox, #6,037 in Research, #36,611 in Software, #68,592 inTools	Review this site: