svm

I'm using scikitlearn in Python to create some SVM models while trying different kernels. The code is pretty simple, and follows the form of: from sklearn import svm clf = svm.SVC(kernel='rbf', C=1, gamma=0.1) clf = svm.SVC(kernel='linear', C=1, gamma=0.1) clf = svm.SVC(kernel='poly', C=1, gamma=0.1) t0 = time() clf.fit(X_train, y_train) print "Training time:", round(time() - t0, 3), "s" pred = clf.predict(X_test) The data is 8 features and a little over 3000 observations. I was surprised to see that rbf was fitted in under a second, whereas linear took 90 seconds and poly took hours. I

I have a multiclass svm classification(6 class). I would like to classify it using LIBSVM. The following are the ones that i have tried and i have some questions regarding them. Method1( one vs one): model = svmtrain(TrainLabel, TrainVec, '-c 1 -g 0.00154 -b 0.9'); [predict_label, accuracy, dec_values] = svmpredict(TestLabel, TestVec, model); Two questions about this method: 1) is that all i need to do for multiclass problem 2) what value should it be for n in '-b n'. I m not sure Method 2( one vs rest): u=unique(TrainLabel); N=length(u); if(N>2) itr=1; classes=0; while((classes~=1)&&(itr<

I'm trying to implement SVM in opencv for features that I have extracted features by using SIFT. I have extracted features for 2 different objects (each object has features of 10 different images which in total I got more than 3000 features for one object) and I put those features in one file (yaml file).. My problem is: I don't know how to label them? so I need to label these two files (as I said each file is the type of yaml and it contains matrix 3260*128 and the second yaml file for the second object is 3349*128)... So please help me to show me how to label these files in order to train

I'm classifing users with a multiclass svm (one-against-on), 3 classes. In binary, I would be able to plot the distribution of the weight of each feature in the hyperplan equation for different training sets. In this case, I don't really need a PCA to see stability of the hyperplan and relative importance of the features (reudced centered btw). What would the alternative be in multiclass svm, as for each training set you have 3 classifiers and you choose one class according to the result of the three classifiers (what is it already ? the class that appears the maximum number of times or the

So I have followed this guide to train my own pedestrian HOG detector. https://github.com/DaHoC/trainHOG/wiki/trainHOG-Tutorial And it was successful with 4 files generated. cvHOGClassifier.yaml descriptorvector.dat features.dat svmlightmodel.dat Does anyone know how to load the descriptorvector.dat file as a vector? I've tried this but failed. vector<float> detector; std::ifstream file; file.open("descriptorvector.dat"); file >> detector; file.close(); This is something I would like to use eventually. gpu::HOGDescriptor hog(Size(64, 128), Size(16, 16), Size(8, 8), Size(8, 8),9); hog

非线性支持向量机SVM 对于线性不可分的数据集， 我们引入了 核 (参考： 核方法·核技巧·核函数 ) 线性支持向量机的算法如下： 将线性支持向量机转换成 非线性支持向量机 只需要将 变为 核函数 即可： 非线性支持向量机的算法如下： 来源： https://www.cnblogs.com/hichens/p/11875506.html

I want to train a SVM for object detection. At this point I have a python script which detects FAST keypoints and extracts BRIEF features at that location. Now I don't know how to use these descriptors to train a SVM. Would you tell me please: How to use the descriptors to train the SVM (As far as I know these descriptors should be my train data)? What are labels used for and how I can get them? To train a SVM you would need a matrix X with your features and a vector y with your labels. It should look like this for 3 images and two features: >>> from sklearn import svm >>> X = [[0, 0], <-

We can use svm.SVC.score() to evaluate the accuracy of the SVM model. I want to get the predicted class and the actual class in case of wrong predictions. How can I achieve this in scikit-learn ? The simplest approach is just to iterate over your predictions (and correct classifications) and do whatever you want with the output (in the following example I will just print it to stdout). Lets assume that your data is in inputs, labels, and your trained SVM is in clf, then you can just do predictions = clf.predict(inputs) for input, prediction, label in zip(inputs, predictions, labels): if