About this document
Support Vector Machine Overview and Implementation by Jyothi upadhyay is a document available to read on EtoBox.
Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression. It finds a hyperplane that maximizes the margin between different classes of data to create an optimal boundary for classification. SVM can be linear for linearly separable data or use kernels to transform non-linear data into a higher dimension for classification. SVM has applications in areas like face detection, text categorization, speech recognition, image classification, cancer diagnosis, an
- Author
- Jyothi upadhyay
- Language
- EN