From 2f9d82984a83850145c7581a84f509be5fe51749 Mon Sep 17 00:00:00 2001 From: sofiane Date: Mon, 10 Jul 2023 11:49:49 +0200 Subject: [PATCH] ADD: correct fault --- doc/theoretical_description_multilabel_classification.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/theoretical_description_multilabel_classification.rst b/doc/theoretical_description_multilabel_classification.rst index 287e679f0..75365cad5 100644 --- a/doc/theoretical_description_multilabel_classification.rst +++ b/doc/theoretical_description_multilabel_classification.rst @@ -168,7 +168,7 @@ throught multiple hypothesis testing. We can express the goal of the procedure a In order to find all the parameters :math:`\lambda` that satisfy the above condition, Learn Then Test propose to do the following: 0: First across the collections of functions :math:`(T_\lambda)_{\lambda\in\Lambda}`, we estimate the risk on the calibration data -\{(x_1, y_1), \ldots, (x_n, y_n)\}`. +\{(x_1, y_1), \dots, (x_n, y_n)\}`. 1: For each :math:`\lambda_j` in a discrete set :math:`\Lambda = \{\lambda_1, \lambda_2,\dots, \lambda_n\}`, we associate the null hypothesis :math:`\mathbb{H}_j: R(\lambda_j)>\alpha`, as rejecting the hypothesis corresponds to selecting :math:`\lambda_j` as a point where risk the risk is controlled.