From 2d27588787749272c793e862c9b132076d357751 Mon Sep 17 00:00:00 2001
From: troyyyyy <huwc@lamda.nju.edu.cn>
Date: Tue, 5 Dec 2023 18:51:39 +0800
Subject: [PATCH] [DOC] fix typo

---
 docs/Overview/Abductive-Learning.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/Overview/Abductive-Learning.rst b/docs/Overview/Abductive-Learning.rst
index 1ebdf8d..b0d3d5e 100644
--- a/docs/Overview/Abductive-Learning.rst
+++ b/docs/Overview/Abductive-Learning.rst
@@ -4,7 +4,7 @@ Abductive Learning
 Traditional supervised machine learning, e.g. classification, is
 predominantly data-driven, as shown in the below figure. 
 Here, a set of data examples is given, including training instances 
-:math:`\{x_1,\dots,x_m'}` and corresponding ground-truth labels :math:`\{\text{label}(x_1),\dots,\text{label}(x_m)'}`. 
+:math:`\{x_1,\dots,x_m\}` and corresponding ground-truth labels :math:`\{\text{label}(x_1),\dots,\text{label}(x_m)\}`. 
 These data are then used to train a classifier model :math:`f`, 
 aiming to accurately predict the unseen data instances.