Skip to content

This project involves the implementation of efficient and effective LinearSVC on MNIST data set. The MNIST data comprises of digital images of several digits ranging from 0 to 9. Each image is 28 x 28 pixels. Thus, the data set has 10 levels of classes.

Notifications You must be signed in to change notification settings

ibodumas/linearSVM

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

5 Commits
 
 
 
 
 
 

Repository files navigation

linearSVM

This project involves the implementation of efficient and effective LinearSVC on MNIST data set. The MNIST data comprises of digital images of several digits ranging from 0 to 9. Each image is 28 x 28 pixels. Thus, the data set has 10 levels of classes.

SVC in Sklearn implement the “one-against-one” methodology for multi-class classification. Hence given n classes, n*(n - 1)/2 classifiers would be modeled. Thus, in the case of MNIST dataset, there will be a total of 45 classifiers.

To provide a consistent interface with other classifiers, the decision_function_shape option allows aggregating the results of the “one-against-one” classifiers to a decision function of shape (n_samples, n_classes).

For LinearSVC, performs “one-vs-the-rest” multi-class classification. Hence, a total of n classifier, However, only one classifier will be trained if there are two classes. Thus, in this project 10 classifiers are modeled for LinearSVC.

About

This project involves the implementation of efficient and effective LinearSVC on MNIST data set. The MNIST data comprises of digital images of several digits ranging from 0 to 9. Each image is 28 x 28 pixels. Thus, the data set has 10 levels of classes.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published