svm

问题 I have a set of images. I would like to learn a one class SVM (OC-SVM) to model the distribution of a particular class (positive) as I dont have enough examples to represent the other classes (negative). What I understood about OC-SVM is that it tries to separate the data from the origin or in other words it tries to learn a hyper sphere to fit the one class data. My questions are, If I want to use the output of the OC-SVM as a probability estimate, how can I do it? What is the difference

问题 I'm porting an algorithm that uses a Support Vector Machine from Python (using scikit-learn) to C++ (using the machine learning library of OpenCV). I have access to the trained SVM in Python, and I can import SVM model parameters from an XML file into OpenCV. Since the SVM implementation of both scikit-learn and OpenCV is based on LibSVM, I think it should be possible to use the parameters of the trained scikit SVM in OpenCV. The example below shows an XML file which can be used to initialize

问题 I am an SVM newbie and this is my use case: I have a lot of unbalanced data to be binary classified using a linear SVM. I need to fix the false positives rate at certain values and measure the corresponding false negatives for each value. I am using something like the following code making use of scikit-learn svm implementation: # define training data X = [[0, 0], [1, 1]] y = [0, 1] # define and train the SVM clf = svm.LinearSVC(C=0.01, class_weight='auto') #auto for unbalanced distributions

问题 I am trying to plot the hyperplane for the model I trained with LinearSVC and sklearn. Note that I am working with natural languages; before fitting the model I extracted features with CountVectorizer and TfidfTransformer. Here the classifier: from sklearn.svm import LinearSVC from sklearn import svm clf = LinearSVC(C=0.2).fit(X_train_tf, y_train) Then I tried to plot as suggested on the Scikit-learn website: # get the separating hyperplane w = clf.coef_[0] a = -w[0] / w[1] xx = np.linspace(

问题 I am doing multi-label classification where I am trying to predict correct tags to questions: (X = questions, y = list of tags for each question from X). I am wondering, which decision_function_shape for sklearn.svm.SVC should be be used with OneVsRestClassifier? From docs we can read that decision_function_shape can have two values 'ovo' and 'ovr' : decision_function_shape : ‘ovo’, ‘ovr’ or None, default=None Whether to return a one-vs-rest (‘ovr’) decision function of shape (n_samples, n

问题 I'm using libsvm in C-SVC mode with a polynomial kernel of degree 2 and I'm required to train multiple SVMs. During training, I am getting either one or even both of these warnings for some of the SVMs that I train: WARNING: using -h 0 may be faster * WARNING: reaching max number of iterations optimization finished, #iter = 10000000 I've found the description for the h parameter: -h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1) and I've tried to read the explanation

问题 I have used following set of code: And I need to check accuracy of X_train and X_test The following code works for me in my classification problem over multi-labeled class import numpy as np from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import CountVectorizer from sklearn.svm import LinearSVC from sklearn.feature_extraction.text import TfidfTransformer from sklearn.multiclass import OneVsRestClassifier X_train = np.array(["new york is a hell of a town", "new york

问题 I am trying to use the java bindings for libsvm: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ I have implemented a 'trivial' example which is easily linearly separable in y. The data is defined as: double[][] train = new double[1000][]; double[][] test = new double[10][]; for (int i = 0; i < train.length; i++){ if (i+1 > (train.length/2)){ // 50% positive double[] vals = {1,0,i+i}; train[i] = vals; } else { double[] vals = {0,0,i-i-i-2}; // 50% negative train[i] = vals; } } Where the first

问题 I've been playing with some SVM implementations and I am wondering - what is the best way to normalize feature values to fit into one range? (from 0 to 1) Let's suppose I have 3 features with values in ranges of: 3 - 5. 0.02 - 0.05 10-15. How do I convert all of those values into range of [0,1]? What If, during training, the highest value of feature number 1 that I will encounter is 5 and after I begin to use my model on much bigger datasets, I will stumble upon values as high as 7? Then in

问题 I've been playing with some SVM implementations and I am wondering - what is the best way to normalize feature values to fit into one range? (from 0 to 1) Let's suppose I have 3 features with values in ranges of: 3 - 5. 0.02 - 0.05 10-15. How do I convert all of those values into range of [0,1]? What If, during training, the highest value of feature number 1 that I will encounter is 5 and after I begin to use my model on much bigger datasets, I will stumble upon values as high as 7? Then in