SVM Algorithm In Machine Learning

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SVM Algorithm In Machine Learning
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Table Of Content

1. What is SVM?
2. Why is SVM Important?
3. How does SVM work?
4. Applications of SVM

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What is SVM?
A support vector machine is a supervised machine
learning algorithm that divides data into two
categories and then conducts classification or
regression tasks using that division. The
division that is close to the limits of each
group equally is the best one.
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Why is SVM Important?
Because Support vector machines work on an
extreme, it is an extremely aggressive
algorithm. Support vector machines use these
vectors to build a study. Due to this
characteristic, Support vector machines are
unique and different from other algorithms,
which explains why it outperforms Non-SVM.
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How does SVM work?
A Support vector machines model is a
multidimensional space hyperplane representing
various classifications. SVM will iteratively
generate the hyperplane to reduce error. Support
vector machines aim to classify datasets to
identify a maximum marginal hyperplane.
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Applications of SVM

• Web Pages
• Text and hypertext categorization
• Fraud Detection
• Face Detection
• Email Classification
• Bioinformatics
• Generalized predictive control
• Classification of images
• Handwriting Recognition
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