filmov
tv
The Kernel Trick in Support Vector Machine (SVM)
Показать описание
SVM can only produce linear boundaries between classes by default, which not enough for most machine learning applications. In order to get nonlinear boundaries, you have to pre-apply a nonlinear transformation to the data. The kernel trick allows you to bypass the need for specifying this nonlinear transformation explicitly. Instead, you specify a "kernel" function that directly describes how each points relate to each other. Kernels are much more fun to work with and come with important computational benefits.
---------------
Credit:
This video would not have been possible without the help of Gökçe Dayanıklı.
---------------
Credit:
This video would not have been possible without the help of Gökçe Dayanıklı.
0:03:18
The Kernel Trick in Support Vector Machine (SVM)
0:07:30
The Kernel Trick - THE MATH YOU SHOULD KNOW!
0:09:46
What is Kernel Trick in Support Vector Machine | Kernel Trick in SVM Machine Learning Mahesh Huddar
0:01:40
Kernel Trick
0:02:37
The Kernel Trick
0:05:17
MFML 099 - SVMs and the kernel trick
0:07:15
Support Vector Machines Part 2: The Polynomial Kernel (Part 2 of 3)
0:20:32
Support Vector Machines Part 1 (of 3): Main Ideas!!!
0:11:51
Ch 5
0:05:52
Machine Learning: Support Vector Machine - Kernel Trick
0:12:02
SVM Kernels : Data Science Concepts
0:13:35
Ch9 SVM the kernel trick
0:10:41
Kernel Trick for Support Vector Machines [Lecture 3.4]
0:11:16
The Kernel Trick in Support Vector Machine (SVM) || SVM Machine Learning Classifier
0:16:20
The Kernel Trick
0:03:29
1 3 1 The Kernel Trick
0:14:00
Kernel Trick in Support Vector Machine (SVM)
0:10:40
Kernel Trick in Support Vector Machine (SVM) | explained with visualization
0:02:19
Support Vector Machine (SVM) in 2 minutes
0:06:09
Explain Kernel Trick for SVM
0:12:50
Machine Learning #74 - Support Vector Machines #5 - Nicht lineare SVM und der Kernel Trick
0:06:54
Machine Learning 101 | The Kernel Trick
0:28:11
Part 25-Support Vector Machines, the Kernel trick
0:06:18
Kernel Trick in SVM | Geometric Intuition
Комментарии