Find users

Kernel Functions - Pattern Recognition - Lecture Slides

Slides, Advanced Engineering Dynamics

Post: April 19th, 2013
Description
The key points are: Kernel Functions, Mapping Implicitly, Nonlinear Classifiers, Regression Problems, Kernel Trick, Map Pattern Vectors, Fisher Discriminant, Non-Linear Versions, Support Vector Regression, Loss Function, Empirical Risk
The key points are: Kernel Functions, Mapping Implicitly, Nonlinear Classifiers, Regression Problems, Kernel Trick, Map Pattern Vectors, Fisher Discriminant, Non-Linear Versions, Support Vector Regression, Loss Function, Empirical Risk
-
Embed this document

Report Report

Reason:

Send Message

Login or register to download this document!

If you are already registered, login otherwise Register , it just takes 1 minute!

Uploaded by:

padmaja

padmaja
Universityuni_20documents_40doc_answ
Embed this document
Get the App
Contents
• We have been discussing SVM method for learning classifiers. PR NPTEL course – p.1/135 • • We have been discussing SVM method for learning classifiers. The basic idea is to transform the feature space and learn a linear classifier in the new space. PR NPTEL course – p.2/135 • • • We have been discussing SVM method for learning classifiers. The basic idea is to transform the feature space and learn a linear classifier in the new space. Using Kernel functions we can do this mapping implicitly. PR NPTEL course – p.3/135 • • • • We have been discussing SVM method for learning classifiers. The basic idea is to transform the feature space and learn a linear classifier in the new space. Using Kernel functions we can do this mapping implicitly. Thus Kernels give us an elegant method to learn nonlinear classifiers. PR NPTEL course – p.4/135 • • • • • We have been discussing SVM method for learning classifiers. The basic idea is t..

Docsity.com

Learning becomes social!

Authentication required

This feature is reserved for registered user

Register Login

Docsity.com

Learning becomes social!

Authentication required

Hi!
In order to freely download all the documents on Docsity, please register or login:

Register Login