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What algorithm should be used to find the plane for a linearly separable dataset? I know that this is a quadratic programming problem, but I can’t find a suitable algorithm, could you please help?

Problem:

X = $\{x_1, ..., x_n\}$ - dataset, $Y = \{y_1, ..., y_n\}$ - classes. $y_i \in \{1, -1\}, x_i \in \mathbb{R^n}$. You should find vector $(a_1, ..., a_n, b) $ : $\forall i = \overline{1,n} \ sign((a_i, x_i) + b) = y_i$.

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  • $\begingroup$ you tagged it with "svm," though! $\endgroup$ – Taylor Jul 14 '19 at 20:12
  • $\begingroup$ I might be missing something! But why wouldn't you use the logistic regression? Its definition is to separate linearly separable data points. $\endgroup$ – aghd Jul 14 '19 at 20:36
  • $\begingroup$ The two most popular methods would be logistic regression (followed by appropriate thresholding of the predicted probabilities) and support vector machines. $\endgroup$ – user20160 Jul 14 '19 at 21:53
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I would go for linear discriminate analysis , a method used in statistics, pattern recognition and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier.

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