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Data Mining_Anoop Chaturvedi

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Swayam Prabha

Course Title

Multivariate Data Mining- Methods and Applications

Lecture 37

Support Vector Machine for Linear Separable Cases

By

Anoop Chaturvedi

Department of Statistics, University of Allahabad

Prayagraj (India)

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Slides can be downloaded from https://sites.google.com/view/anoopchaturvedi/swayam-prabha

Support Vector Machine (SVM)

• A powerful machine learning algorithm for classification tasks.
• Objective is to find the optimal hyperplane that separates data points of different classes in a feature space.
• Separating hyperplane maximizes the margin between the closest data points of different classes, known as support vectors.

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Applications ⇒ Handwritten digit recognition, text categorization, cancer classification using microarray, expression data, protein secondary-structure prediction.

• Can be used when class boundaries are linear or non linear (kernel SVM).
• Can be applied for binary classification or multiclass classification.
• Techniques are based upon optimization of a convex loss function under given constraints and so are unaffected by problems of local-minima.�

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