Question

SVM uses which method for pattern analysis in High dimensional space- 1. Multi-Linear Regression 2. Classification 3.Kerner 4.Logistic Regression

Answer 1

4..logistic  regression is the right answer

Logistic regression is used when the response variable is categorical in nature  and in high dimension space things are of different categories and different natures that's why in space SVM uses logistic regression.

In machine learning, support vector machines(SVM) are supervised learning models with associated logistic regression that analyze data used for classification and regression analysis.

Answer 2

SVM uses Kernel method for pattern analysis in High dimensional space.

Explanation:

• In most of the machine learning methods, kernel methods are utilized for doing pattern analysis. These methods are a class of algorithms and its best known member is SVM.  

• The primary task of pattern analysis is to discover and study the generic type of relations in the given datasets.  

• Some of the examples are rankings, cluster, correlations, classifications, and certain principal components.

• Kernel methods always require user specified kernel i.e. similarity function.

Learn more about Kernel method

"In Kernel trick method, We do not need the coordinates of the data in the feature space" This statement is True or False?

https://brainly.in/question/8305811

Hich technique implicitly defines the class of possible patterns by introducing a notion of similarity between data?

https://brainly.in/question/7321350