I’ve had to deal with this recently, and here’s what I ended up doing to get SVM to work for images. To train your SVM on a set of images, first you have to construct the training matrix for the SVM. This matrix is specified as follows: each row of the matrix corresponds to one … Read more
I would expect soft-margin SVM to be better even when training dataset is linearly separable. The reason is that in a hard-margin SVM, a single outlier can determine the boundary, which makes the classifier overly sensitive to noise in the data. In the diagram below, a single red outlier essentially determines the boundary, which is … 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
Support Vector Machines are an optimization problem. They are attempting to find a hyperplane that divides the two classes with the largest margin. The support vectors are the points which fall within this margin. It’s easiest to understand if you build it up from simple to more complex. Hard Margin Linear SVM In a training … Read more
Judging from the examples you provide, I’m assuming that by ANNs, you mean multilayer feed-forward networks (FF nets for short), such as multilayer perceptrons, because those are in direct competition with SVMs. One specific benefit that these models have over SVMs is that their size is fixed: they are parametric models, while SVMs are non-parametric. … Read more