From ce4b3638da6117cb408b7776667226c1ca2a5ceb Mon Sep 17 00:00:00 2001 From: Srihari Thyagarajan Date: Mon, 26 Aug 2024 09:09:19 +0530 Subject: [PATCH] Update README --- easy/Linear_Kernel/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/easy/Linear_Kernel/README.md b/easy/Linear_Kernel/README.md index 3e75569..f3aacb8 100644 --- a/easy/Linear_Kernel/README.md +++ b/easy/Linear_Kernel/README.md @@ -40,7 +40,7 @@ A kernel function in machine learning is used to measure the similarity between The linear kernel between two vectors $\mathbf{x}_1$ and $\mathbf{x}_2$ is mathematically defined as: -$$ K(\mathbf{x}_1, \mathbf{x}_2) = \mathbf{x}_1 \cdot \mathbf{x}_2 = \sum_{i=1}^{n} x_{1,i} \cdot x_{2,i} $$ +$ K(\mathbf{x}_1, \mathbf{x}_2) = \mathbf{x}_1 \cdot \mathbf{x}_2 = \sum_{i=1}^{n} x_{1,i} \cdot x_{2,i} Where $n$ is the number of features, and $x_{1,i}$ and $x_{2,i}$ are the components of the vectors $\mathbf{x}_1$ and $\mathbf{x}_2$ respectively.