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Support Vector Machine refers to "a set of related supervised learning methods that analyze data and recognize patterns, used for classification and regression analysis."
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Which approach can I use to train SVMs with a hundred of thousand of examples?
I am using SVMs to learn models. Every time want to use them on a "real life" data set, I see that they take forever to run.
I found that the computational complexity is
O(n_samples^2 * n_features) …
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Why does SVM needs to keep support vectors?
I am reading the book Artificial Intelligence a Modern Approach and I have trouble understanding why the SVM needs to keep support vectors. …
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Remove features with high correlation
In a classification problem using Linear SVM, I am trying to remove variables which have a strong correlation (Pearson) between them from a dataset.
What is the usual threshold recommended? …