Yes, there is attribute coef_ for SVM classifier but it only works for SVM with linear kernel. For other kernels it is not possible because data are transformed by kernel method to another space, which is not related to input space, check the explanation. from matplotlib import pyplot as plt from sklearn import svm def … Read more
While online algorithms for SVMs do exist, it has become important to specify if you want kernel or linear SVMs, as many efficient algorithms have been developed for the special case of linear SVMs. For the linear case, if you use the SGD classifier in scikit-learn with the hinge loss and L2 regularization you will … Read more
In mathematical sense you need to set: SVC(kernel=”linear”, **kwargs) # by default it uses RBF kernel and LinearSVC(loss=”hinge”, **kwargs) # by default it uses squared hinge loss Another element, which cannot be easily fixed is increasing intercept_scaling in LinearSVC, as in this implementation bias is regularized (which is not true in SVC nor should be … Read more
The most basic approach here is to use so called “class weighting scheme” – in classical SVM formulation there is a C parameter used to control the missclassification count. It can be changed into C1 and C2 parameters used for class 1 and 2 respectively. The most common choice of C1 and C2 for a … Read more
In order to detect arbitrary objects with using opencv HOG descriptors and SVM classifier, you need to first train the classifier. Playing with the parameters will not help here, sorry 🙁 . In broad terms, you will need to complete the following steps: Step 1) Prepare some training images of the objects you want to … Read more
Mainly there are two reasons we do cross-validation: as a testing method which gives us a nearly unbiased estimate of the generalization power of our model (by avoiding overfitting) as a way of model selection (eg: find the best C and gamma parameters over the training data, see this post for an example) For the … Read more
You seem to already have figured out the problem… According to the README file included in the MATLAB package: To use precomputed kernel, you must include sample serial number as the first column of the training and testing data. Let me illustrate with an example: %# read dataset [dataClass, data] = libsvmread(‘./heart_scale’); %# split into … Read more
I wrote a child class of CvSVM to extract primal form after a linear svm is trained. Positive samples are labeled 1 and negative samples are labeled -1. It is strange that I have to put negative sign in front of alphas and leaving the sign of rho unchanged in order to get correct results … Read more
The -v option here is really meant to be used as a way to avoid the overfitting problem (instead of using the whole data for training, perform an N-fold cross-validation training on N-1 folds and testing on the remaining fold, one at-a-time, then report the average accuracy). Thus it only returns the cross-validation accuracy (assuming … Read more
I don’t fully understand your code, but let’s go trough the example of the documentation page you referenced: import numpy as np X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]]) y = np.array([1, 1, 2, 2]) from sklearn.svm import SVC clf = SVC() clf.fit(X, y) Now let’s apply both the decision function and … Read more