Support Vector Machines (SVM) w/ Sequential Minimal Optimization (SMO) +Python

Support Vector Machines w/ Python & SMO (Sequential Minimal Optimization)

prototypeprj.com = zaneacademy.com (version 2.0)

00:06 demo a prebuilt version of the application  01:37 code the application  01:49 go over the training data used in this app.  02:47 go over the various classes that make up the app.  03:26 quick introduction to Support Vector Machine (SVM)  04:58 quick introduction to Sequential Minimal Optimization (SMO)  06:02 code the SupportVectorMachines class  07:24 plug in equation for 'w' into equation of a linear svm  and use resulting equation in code  08:08 EPSILON and slack penalty C  08:30 alpha and violating KKT conditions 09:34 selecting the index of the 2nd alpha to optimize  11:30 optimize an alpha pair and b  13:15 define and calculate w  13:37 classify method  14:00 display information tables code  14:40 handle command line entry code  14:49 plot data + decision boundary + support vectors code  15:34 explain + test run application  16:40 change C and rerun app. to show overfitting ---------------------------------------------------------------------------------------------------------------------------- quickly download, setup, and run 'Support Vector Machines (SVM)  w/ Sequential Minimal Optimization (SMO) + Python' ----------------------------------------------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------------------------- download source code @ https://sites.fastspring.com/prototypeprj/instant/ai -----------------------------------------------------------------------------------------------------------

Support Vector Machines (SVM) w/ JAVA & Sequential Minimal Optimization (SMO)

Support Vector Machines w/ JAVA & SMO (Sequential Minimal Optimization)

prototypeprj.com = zaneacademy.com (version 2.0)

00:07 demo a prebuilt version of the application 01:50 code the application  02:11 go over the various classes that make up the app.  03:08 go over the training data used in this app.   03:52 setup and use the commons math jar to do matrix operations 04:33 start the initial coding of the SupportVectorMachines class  05:20 quick introduction to Support Vector Machine (SVM)  06:46 quick introduction to Sequential Minimal Optimization (SMO)  07:50 resume coding the application SupportVectorMachines class  09:41 plug in equation for 'w' into equation of a linear svm and use resulting equation in code 10:40 EPSILON and slack penalty C   11:02 alpha and the violation of KKT conditions  11:54 selecting the index of the 2nd alpha to optimize  13:26 optimize an alpha pair and b  15:15 define and calculate w  15:37 classify method  16:12 display information tables code  16:54 handle command line entry code  17:12 JavaFX display code  18:14 explain + test run the application  19:27 change C and rerun app. to show overfitting ----------------------------------------------------------------------------------------------------------------------------- quickly download, import into Eclipse, and run zip file for 'Support Vector Machines w/ JAVA & SMO (Sequential Minimal Optimization)' ------------------------------------------------------------------------------------------------------------------------------- ----------------------------------------------------------------------------------------------------------- download source code @https://sites.fastspring.com/prototypeprj/instant/ai -----------------------------------------------------------------------------------------------------------