Confidence interval for expected prediction error from cross-validation
I am using a support vector machine for binary classification on a sample of size 150 (75 of each class). I am using 5-fold stratified cross-validation to estimate the expected prediction error, i.e....
View ArticleSVM and concatenation of features
For example I train my SVM on 3 features set of different size: Training data size [rows cols]: features1 [nsamples 100] features2 [nsamples 50] features3 [nsamples 128] And for each feature set I get...
View ArticleHow to calculate decision boundary from support vectors?
I want to obtain decision boundary of SVM using OpenCV 2.4.11, but it seems that it’s not returning it explicitly, but only support vectors. How we can calculate decision boundary from support vectors?...
View ArticleFeature Normalization & Learning
I’m working on a cell classifier (as in Biological Cells) using images obtained by microscope. Right now I have about 12 Features written (color,width-height ratio, shape, couple of texture features,...
View ArticleMethods for supervised regression learning
What ML methods should I try for a data set of around 1000 samples? The output variable is dependent on say 10 regressors of which 8 are real numbers and the other 2 are categorical. Or they might just...
View ArticleHow to install libsvm extention on Ubuntu? [closed]
I use libsvm on Linux and it works (although I don’t know Linux and I use it only for that). Now, I’m trying to extend svm procedure to deal with ordinal regression as here:...
View ArticleHigh accuracy during cross validation, low accuracy on test set
I’m currently trying to build a tennis prediction model. Unfortunately, I have some issues that I hope you could help me to handle. I have 1110 examples of matches from the year 2013, with their...
View Articlelibsvm to liblinear migration
I’ve reached the point when svmlib works too long so I decided to switch to liblinear instead. My question is how to properly transfer parameter training from libsvm to liblinear Say, if I have the...
View ArticleSupport Vector Machine Optimization Convexity
The SVM derivation is centered on convex optimization. By definition, convex optimization requires a convex objective function and convex or linear constraints. The task is to minimize this function....
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Optimizing a Support Vector Machine with Quadratic Programming
I’m trying to understand the process for training a linear support vector machine. I realize that properties of SMVs allow them to be optimized much quicker than by using a quadratic programming...
View ArticleSupport Vector Machine vs. Decision Tree performance [closed]
I hope anyone could give me some advice here, I would really appreciate. I am trying to classify a binary class problem using SVM but I found that highest accuracy rate from SVM (using loose grid and...
View ArticleHow can I formulate soft margin primal SVM constraints for use in...
The minimization equation here is min ((1/2) ||w||2 + C (summation of epsilon(i)) w,b subject to the constraint yi(wxi-b) >= 1 – eplison (i) >= 0.
View ArticleCustomer data-checking preference of customers for time of meal
If you have to check for preference of customers w.r.t. lunch and snacks given the data regarding time of order? I have used a prob. of ordering as lunch or snack as 50% and third quantile of total...
View ArticleLarge Margin Classifier with CVXOPT
I need to generate a Large Margin Classifier using python library cvxopt which allows me to solve the quadratic program. I am trying to write a python function to take the training data and some test...
View Articlehow to find outliers from high-dimensional data set?
The data has about 40 features and 500,000 instances. And the data is sparse. I wish to fit a svm model with the data. To fit svm, I need to first scale the data. However, if the data contains many...
View ArticleTesting an SVM performance – unnormalized data performing better than normalized
I’m training a SVM on a dataset in OpenCV that contains 14 features and thousands of observations. I understand that for optimal performance, it is recommended to perform principal component analysis...
View ArticleRademacher complexity of SVM with kernel in terms of whole Kernel Matrix
http://www.cs.nyu.edu/~mohri/mls/lecture_5.pdf In slide no. 18 here, it is shown that Rademacher complexity of SVM with kernel can be written in terms of trace of the matrix. Are there any other...
View ArticleRun-Time difference between linear and RBF kernels in SVM
I’m using kernlab package in R. I trained an SVM using linear kernel and RBF kernel with same data set (the number of instances is 3000). When I use a linear kernel, it takes much longer to train the...
View ArticleHigh difference between cv rate and classification accuracy in libsvm
edit: nevermind I solved it I am training an svm on a dataset with 5 classes using libsvm. I have a training set and a test set. I am using the easy.py script. The accuracy is a lot worse than the cv...
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Data Visualization after k-fold Cross Validation step
I need suggestion about data visualisation for cross validated data. I want to plot data after cross validation and need suggestions how to do that? I am thinking to plot like this If I use a...
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