Support Vector Machine SVM
Tags: #machine learning #svmEquation
$$\max_{w,b} \frac{2}{||w||} \\ s.t.\ y_{i}(w^{T}x_{i} + b) \geq 1, i=1,2,...,m \\ L(w,b,\alpha)=\frac{1}{2}||w||^2 + \sum^{m}_{i=1}a_{i}(1-y_{i}(w^{T}x_{i} + b))$$Latex Code
\max_{w,b} \frac{2}{||w||} \\
s.t.\ y_{i}(w^{T}x_{i} + b) \geq 1, i=1,2,...,m \\
L(w,b,\alpha)=\frac{1}{2}||w||^2 + \sum^{m}_{i=1}a_{i}(1-y_{i}(w^{T}x_{i} + b))
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Introduction
Equation
Find optimal hyper plane
Dual problem Lagrangian Relaxation
Latex Code
\max_{w,b} \frac{2}{||w||} \\
s.t.\ y_{i}(w^{T}x_{i} + b) \geq 1, i=1,2,...,m \\
L(w,b,\alpha)=\frac{1}{2}||w||^2 + \sum^{m}_{i=1}a_{i}(1-y_{i}(w^{T}x_{i} + b))
Explanation
Latex code for Support Vector Machine (SVM).
: Dual problem Lagrangian Relaxation
: Weight of Linear Classifier
: Classifier
: Decision Boundary
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