Overview Of Smo And Other Algorithms For Svm; V Svm And V Svr; Svm As A Risk Minimizer Exam Study Material for Gate ECE

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Introduction to nonlinear Fiber Optics	lec32	05:36 to 10:00	PDF	VIDEO
Introduction to nonlinear Fiber Optics	lec32	20:43 to 31:14	PDF	VIDEO
Introduction to nonlinear Fiber Optics	lec32	31:14 to 35:30	PDF	VIDEO
Introduction to nonlinear Fiber Optics	lec32	35:30 to 40:12	PDF	VIDEO
Introduction to nonlinear Fiber Optics	lec32	40:12 to 47:37	PDF	VIDEO
Introduction to nonlinear Fiber Optics	lec32	47:37 to upto	PDF	VIDEO
Non Linear Schrodinger Equation	lec33	08:50 to 13:12	PDF	VIDEO
Cross Phase Modulation and four wave mixing	lec38	01:42 to 09:04	PDF	VIDEO
Wave Model I	lec5	02:10 to 06:27	PDF	VIDEO
Wave Model I	lec5	06:27 to 08:58	PDF	VIDEO

Overview of Statistical Learning Theory; Empirical Risk Minimization	lec21	42:57 to 44:11	PDF	VIDEO
Overview of Statistical Learning Theory; Empirical Risk Minimization	lec21	45:51 to 47:35	PDF	VIDEO
Support Vector Regression and E-insensitive Loss function, examples of SVM learning	lec35	08:17 to 09:19	PDF	VIDEO
Support Vector Regression and E-insensitive Loss function, examples of SVM learning	lec35	09:19 to 11:14	PDF	VIDEO
Support Vector Regression and E-insensitive Loss function, examples of SVM learning	lec35	11:14 to 12:03	PDF	VIDEO
Support Vector Regression and E-insensitive Loss function, examples of SVM learning	lec35	28:13 to 30:13	PDF	VIDEO
Support Vector Regression and E-insensitive Loss function, examples of SVM learning	lec35	30:13 to 31:40	PDF	VIDEO
Overview of SMO and other algorithms for SVM; v-SVM and v-SVR; SVM as a risk minimizer	lec36	52:50 to 54:11	PDF	VIDEO
Overview of SMO and other algorithms for SVM; v-SVM and v-SVR; SVM as a risk minimizer	lec36	55:56 to 57:10	PDF	VIDEO
Risk minimization view of AdaBoost	lec42	21:10 to 22:57	PDF	VIDEO