How does the classification of a feature set in SVM depend on the sign of the decision function (text{sign}(mathbf{x}_i cdot mathbf{w} + b))?
Support Vector Machines (SVMs) are a powerful supervised learning algorithm used for classification and regression tasks. The primary goal of an SVM is to find the optimal hyperplane that best separates the data points of different classes in a high-dimensional space. The classification of a feature set in SVM is deeply tied to the decision
- Published in Artificial Intelligence, EITC/AI/MLP Machine Learning with Python, Support vector machine, Support vector machine optimization, Examination review
What is the formula used in the 'predict' method to calculate the classification for each data point?
The 'predict' method in the context of Support Vector Machines (SVMs) is used to determine the classification for each data point. To understand the formula used in this method, we need to first grasp the underlying principles of SVMs and their decision boundaries. SVMs are a powerful class of supervised learning algorithms that can be
- Published in Artificial Intelligence, EITC/AI/MLP Machine Learning with Python, Support vector machine, Creating an SVM from scratch, Examination review
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.