Size: N/A License: Shareware Price: $199.00 By: 9 and graphics created in macromedia Flash/micromedia flash. Size: 1.56MB License: Freeware Price: Free By: 12 CorelDRAW graphics Suite 12 introduces smart design tools for producing more cre.lity all at an unbeatable price!corel 11 free corel drow 11 mac. Size: 499.46MB License: Shareware Price: $499.00 By: 10.0.0 Build 1295980 Comprehensive Windows 7 support, including Easy install, 3D graphics Support, li.Open GL support for Linux and a new web interface to access virtual machines, it. Clip art free downloads for mac. Flash also supports high-fidelity MP3 streaming audio, text entry fields, interactive interfaces. This dataset was collected by botanist Anderson and contains random samples of flowers belonging to three species of iris flowers setosa, versicolor, and virginica. For each of the species, 50 observations for sepal length, sepal width, petal length, and petal width are recorded. Sample Data Sets. Statistics and Machine Learning Toolbox™ software includes the sample data sets in the following table. To load a data set into the MATLAB ®. Fisher's 1936 iris data: flu.mat: Google Flu Trends estimated ILI (influenza-like illness) percentage for various regions of the US, and CDC weighted ILI percentage based on. Linear, 3 classes Basic procedure to use libsvm: • Preprocess your data. This including normalization (make all values between 0 and 1) and transform non-numeric values to numeric. Matlab Download For Mac![]() Matlab Download For Mac 10.10You can use the following code to normalize (from libsvm webpage): (data - repmat(min(data,[],1),size(data,1),1))*spdiags(1./(max(data,[],1)-min(data,[],1))',0,size(data,2),size(data,2)) • Find optimal parameter values. For linear kernel, you have 1 parameter C (penalize parameter). For commonly used radial kernel, you have two parameters (C and gamma). Different parameter values will yield different accuracy rate. To avoid over fitting, you use n-fold cross validation. Iris Dataset RFor example, a 5-fold cross validation is to use 4/5 of the data to train the svm model and the rest 1/5 to test. The option -c, -g, and -v controls parameter C, gamma and n-fold cross validation. Textpad editor download. A piece of code from libsvm website is: bestcv = 0; for log2c = -1:3, for log2g = -4:1, cmd = ['-v 5 -c ', num2str(2^log2c), ' -g ', num2str(2^log2g)]; cv = svmtrain(heart_scale_label, heart_scale_inst, cmd); if (cv >= bestcv), bestcv = cv; bestc = 2^log2c; bestg = 2^log2g; end fprintf('%g%g%g (best c=%g, g=%g, rate=%g) n', log2c, log2g, cv, bestc, bestg, bestcv); end end • You may have to run the above code several times with different range of parameter values to find the optimal values. Games Download For MacFor example, you might want to start from a bigger range with coarse resolution; then fine tune to smaller regions with higher resolution. • After finding the optimal parameter values, use all data to train your model with your optimal parameter values. Cmd = ['-t 2 -c ', num2str(bestc), ' -g ', num2str(bestg)]; model = svmtrain(l, d, cmd); • If you have new data, you may use this model to classify the new data. [predicted_label, accuracy, decision_values] = svmpredict(zeros(size(dd,1),1), dd, model); Commonly used options • -v n: n-fold cross validation • -t 0: linear kernel • -t 2: radial basis (default) • -s 0: SVC type = C-SVC • -C: C parameter value, default 1 • -g: gamma parameter value libsvm performance I tested on different data size and record the time spent (in second). Computer: Processor: 2×2.66G, memory: 12G, OS: Windows XP installed in VMWare in Mac OS 10.5 data size # features svmtrain svmpredict 100 2 0.00 0.00 100 6 0.00 0.00 100 10 0.00 0.00 100 20 0.00 0.00 100 50 0.01 0.00 100 100 0.02 0.01 500 2 0.02 0.01 500 6 0.03 0.02 500 10 0.05 0.03 500 20 0.08 0.03 500 50 0.46 0.07 500 100 0.56 0.12 1000 2 0.07 0.04 1000 6 0.10 0.06 1000 10 0.15 0.10 1000 20 0.36 0.14 1000 50 1.09 0.30 1000 100 3.07 0.50 It’s fairly fast. Resources: MatLab code to generate the plots above: SVM basics: Download libsvm for matlab at: The meaning of libsvm output is at: http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html#f804. Dear Xu Cui, I found the Matlab code for LIBSVM is very helpful.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. Archives
January 2019
Categories |