From d473147604dd3574ec628e6384a09003726c9629 Mon Sep 17 00:00:00 2001 From: Srihari Thyagarajan Date: Mon, 26 Aug 2024 09:03:54 +0530 Subject: [PATCH] Update README --- easy/Linear_Kernel/README.md | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/easy/Linear_Kernel/README.md b/easy/Linear_Kernel/README.md index b0221ac..d37057b 100644 --- a/easy/Linear_Kernel/README.md +++ b/easy/Linear_Kernel/README.md @@ -40,9 +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. @@ -113,6 +111,6 @@ Both NumPy functions are more efficient and can handle multi-dimensional arrays, To ensure proper rendering of LaTeX equations in various Markdown environments, we'll use inline LaTeX notation. The linear kernel between two vectors $\mathbf{x}_1$ and $\mathbf{x}_2$ is mathematically defined as: -$K(\mathbf{x}_1, \mathbf{x}_2) = \mathbf{x}_1 \cdot \mathbf{x}_2 = \sum_{i=1}^{n} x_{1,i} \cdot x_{2,i}$ + 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.