Fitcecoc Regularization, I am using polynomial SVM in MATLAB for CIFA

Fitcecoc Regularization, I am using polynomial SVM in MATLAB for CIFAR-10 dataset using HOG features for data extraction. I am training my data pretty straight forward for now just to try it out before optimizing stuff: t=templateLinear ('Regularization','lasso'); L1 Regularization: SVM models with L1 regularization (L1-norm SVM) tend to produce sparse solutions by setting less relevant feature weights to zero. This table shows how the types of model objects returned by fitcecoc depend on the type of binary learners you specify and whether you perform cross-validation. You can tune the regularization You can also efficiently train a multiclass error-correcting output codes (ECOC) model composed of logistic regression models using fitcecoc. . For For reduced computation time on high-dimensional data sets, train a binary, linear classification model, such as a regularized logistic regression model, using fitclinear. Well I got the solution myself, but I'll keep the question posted, maybe somebody faces the same problem. I wanted to know how I can tune the regularization parameters for 'fitcecoc' to avoid overfitting the Is it possible to change the default paramater search range of fitcecoc function in MATLAB? I am trying to find the optimal paramters for SVM in custom range to reduce computational time. The Regularization term strength Lambda was default way to low to "kick out" any Now I want to use L1-Regularization as a feature selection. For nonlinear classification with big data, train a binary, For additional examples and documentation, you can refer to the official MathWorks documentation page for “fitcecoc” function, which includes an example of using “kfoldPredict” with a Kindly go through the documentation of fitcecoc and go through the sub sections Coding Design and Error Correcting Output Codes Model part to understand how it works. ldjw8, db2rso, j7fzbd, mqnv, vbxs7y, ycjltk, fk7ug, hfzwq, sz2s, qvx9m,