How do we find the values of vector W and B in SVM?
Support Vector Machines (SVM) is a powerful machine learning algorithm used for classification and regression tasks. In SVM, the goal is to find a hyperplane that maximally separates the data points of different classes. The values of the weight vector (W) and the bias term (B) in SVM are important in determining the position and
- Published in Artificial Intelligence, EITC/AI/MLP Machine Learning with Python, Support vector machine, Support vector assertion, Examination review
What happens if the result of the equation in SVM is exactly zero?
When the result of the equation in a Support Vector Machine (SVM) is exactly zero, it indicates that the data point lies exactly on the decision boundary between the two classes. In other words, the data point is equidistant from the support vectors of both classes. To understand the significance of this, let's first consider
- Published in Artificial Intelligence, EITC/AI/MLP Machine Learning with Python, Support vector machine, Support vector assertion, Examination review
What equation is used to classify new points in SVM?
The equation used to classify new points in Support Vector Machines (SVM) is known as the Support Vector Assertion. SVM is a popular machine learning algorithm used for classification and regression tasks. It is particularly effective in solving complex problems with high-dimensional data. The Support Vector Assertion is derived from the decision function of SVM.
How does SVM determine the position of a new point relative to the decision boundary?
Support Vector Machines (SVM) are a popular machine learning algorithm used for classification and regression tasks. SVMs are particularly effective when dealing with high-dimensional data and can handle both linear and non-linear decision boundaries. In this answer, we will focus on how SVM determines the position of a new point relative to the decision boundary.
How does SVM classify new points after being trained?
Support Vector Machines (SVMs) are supervised learning models that can be used for classification and regression tasks. In the context of classification, SVMs aim to find a hyperplane that separates different classes of data points. Once trained, SVMs can be used to classify new points by determining which side of the hyperplane they fall on.