Support Vector Machine(SVM) is a popular machine learning algorithm for its excellent generalization ability. However, similar to most of traditional algorithms, the proposal of SVM is based on an ...

Kernel functions are central to a Support Vector Machine's effectiveness. Common types include the linear kernel, suitable for linearly separable data; the polynomial kernel, which works well for ...

Computational Complexity: Training SVM can be computationally expensive, especially for large datasets. Difficulty in Interpreting Results: SVM models can be difficult to interpret, especially when ...

In this repository, you will find a comprehensive collection of in-depth explanations, intuition, questions, and answers related to the Support Vector Machine (SVM) algorithm. Additionally, you will ...

Support Vector Machine (SVM) is a powerful machine learning algorithm used for linear or nonlinear classification, regression, and even outlier detection tasks.It is used in such as text ...

The SVM algorithm reduces the influence of external conditions on the calculation results, and further optimizes the construction project cost modeling calculation. By using the SVM algorithm ...

SVM Algorithm Improvement. The core of the SVM algorithm is to find the support vector and the optimal hyperplane (Varatharajan et al., 2018; Zeng et al., 2018; Hu et al., 2020), so the key problem is ...

A Support Vector Machine (SVM) is a supervised learning algorithm utilized in the field of machine learning. It is primarily applied to perform tasks such as classification and regressionThis ...