Скачать или смотреть EP2 Support vector machine in machine learning

In episode 2, we will look at the derivation of the maximum margin and the general equation of the SVM. We also introduce the rationale for kernel functions when you need to deal with non linearity.

Chapters:
00:00 - Introduction by Anirban Ghatak, Founder MieRobot
00:35 - SVM derivation
04:40 - Generalized form of SVM
06:57 - SVM maximum margin separator
09:31 - Weak and strong classifier C(i) term
10:10 - Overfitting with zeta parameter
10:50 - Handling non-linearity with SVM (Kernel trick)
11:48 - Conclusion

To download the white board image used in this below click below
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