Support vector machines (SVMs) are a set of related supervised learning methods that analyze data and recognize patterns, used for classification and regression analysis.
I found that scaling in SVM (Support Vector Machine) problems really improve its performance... I have read this explanation: "The …
machine-learning svm scalingHow can i know sample's probability that it belongs to a class predicted by predict() function of Scikit-Learn in Support …
svm scikit-learnI am using sklearn for multi-classification task. I need to split alldata into train_set and test_set. I want …
machine-learning scikit-learn svm cross-validationI am currently confusing about implementing SVM with cross-validation using Matlab now. There are many post on stackoverflow that mentioned …
matlab svmGiven a linearly separable dataset, is it necessarily better to use a a hard margin SVM over a soft-margin SVM?
algorithm machine-learning svmI have a sentiment analysis task, for this Im using this corpus the opinions have 5 classes (very neg, neg, neu, …
machine-learning nlp scikit-learn svm confusion-matrixI have a project, which I want to detect objects in the images; my aim is to use HOG features. …
c++ opencv svm object-recognition training-dataI have used following set of code: And I need to check accuracy of X_train and X_test The …
python machine-learning svm scikit-learn svcI have an SVM in R and I would now like to plot the classification space for this machine. I …
r graph machine-learning svmI was told to use the caret package in order to perform Support Vector Machine regression with 10 fold cross validation …
r svm r-caret