IEEE Sir Padampat Singhania University

Dr. Manisha Thakkar explained about the following:
SVM used for both Regression and Classification. Idea behind SVM is: Max-Margin classifier, Lagrangian Multipliers, Kernels, Complexity. Hyperplane is linear decision surface that splits the space into two parts. Hyperplane is a binary classifier. Quadratic Programming is an optimization problem where the objective function is quadratic, subject to linear constraints. If data is not linearly separable in the input space, then apply kernel to map data to a higher dimensional space where it is linearly separable. Popular kernels are: Linear kernel, Gaussian Kernel, Exponential kernel, Polynomial kernel, Hybrid kernel, Sigmoidal. Minimize of a function is addition of loss and penalty of a function. Loss measures error of fitting the data. Penalty penalizes complexity of the learned functions. Tuning parameters are: Kernel, Regularization (How much misclassification is tolerated), Gamma (how for the influence of a single training example reaches), Margin. Proteins are synthesized in the cytosol. Transported into different subcellular locations where they carry out their functions. Pros: Empirically achieve excellent results in high-dimensional data with large number of variables and small samples. Cons: SVM doesn’t directly provide probability estimates, these are calculated using cross validation. Applications of SVM are: Image-based analysis and classification tasks, Geo-spatial data-based application, Test-based application, Computational biology, Security-based application, Chaotic system controls.