Question

Which classifier involves finding Optimal hyperplane for linearly separable Patterns?

Answer 1

Support Vector Machine (SVM) Classification.

It is a supervised learning method that can be used for disease diagnosis.

The feature extraction can transform a data from a high-dimensional space to fewer dimensions.

Feature extraction makes use of simplifying the nature of resources that may be needed to quantify a large set of data.

While analysing complex data, one of the main issues start from the number of variables that are in it.

Feature extraction is a term for all those methods of making combinations of variables so that these problems get solved while describing the datasets with enough accuracy.

Hence, it can be used for finding Optimal hyperplane for linearly separable Patterns.

Answer 2

There is a special type known as the SVM. It is an acronym for Support Vector Machine.  

SVM is a supervised learning method that can be used for disease diagnosis.  

The can transform a high-dimension data to a data of fewer dimensional one.  

Feature extraction makes use of simplifying the nature of resources that may be needed to quantify a large set of data.  

It can work on complicated data, the main problem is various variables in that data.  

Feature extraction is a term for all those methods of making combinations of variables so that these problems gets solved while describing the datasets with enough accuracy.  

Hence, it can be used to analyse Optimal hyperplane.