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I learned to Support vector machine (Hyperplane, Support vector, and Margins).
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I learned in this lecture support vector machine (SVM) (hyperplane, support vector, margins).
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In the support vector machine, every line shows the degree of data points and three types of kernel functions that can be used to separate the data points through hyperplane boundaries. (1) Linear Kernel (2) RBF-Radial Basis Function Kernel (3) Polynomial Kernel
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AOA,I learned in this lecture about SUPPORT VECTOR MACHINE ( S.V.M ) and its types
TYPES OF SUPPORT VECTOR MACHINE ( S.V.M ) ( apply for linear and nonlinear data )1-Linear Support Vector Machine (SVM)
2-Polynomial Support Vector Machine
3-Radial Basis Function (RBF) Support Vector Machine
And also learned about applications of SVM, which are1: Image classification
2: Classification for text
3: Protein classification
4: Handwriting
And also learned about1: Hyper plane
2; Support vector
3: Margins
ALLAH PAK aap ko sahat o aafiat wali lambi umar ata kray aor ap ko dono jahan ki bhalian naseeb farmaey Ameen.
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