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FAQ:
Q: What are the disadvantages of SVM algorithms?
A: SVM Disadvantages. Choosing a “good” kernel function is not easy. Long training time for large datasets. Difficult to understand and interpret the final model, variable weights and individual impact. Since the final model is not so easy to see, we can not do small calibrations to the model hence its tough to incorporate our business logic.
Q: How many kinds of SVM algorithms exist?
A: Online or offline? Classification (SVC) or regression (SVR) Binary or multi class Primal or dual formulation Regularization: L1,L2,…. Loss: Hinge Loss,square loss,logistic loss,…. Kernelized or not? … Optimization algorithm: SGD,simplex,coordinate descent,cutting plane,interior point,etc….
Q: How do you implement SVM algorithm in MATLAB?
A: Understanding Support Vector Machines. … Using Support Vector Machines. … Train SVM Classifiers Using a Gaussian Kernel. … Train SVM Classifier Using Custom Kernel. … Optimize an SVM Classifier Fit Using Bayesian Optimization. … Plot Posterior Probability Regions for SVM Classification Models. … Analyze Images Using Linear Support Vector Machines. …
Q: When to use SVM?
A: Regarding the shrinking parameter,quoting 12: We found that if the number of iterations is large,then shrinking can shorten the training time. … Parameter nu in NuSVC/OneClassSVM/NuSVR approximates the fraction of training errors and support vectors. In SVC,if the data is unbalanced (e.g. …