Support Vector Machines (SVM) is a supervised machine learning algorithm for classification and regression tasks. SVM works by finding the optimal hyperplane that separates data points of different classes in a high-dimensional space. The goal is to maximize the margin, which is the distance between the closest data points (called support vectors) from each class and the separating hyperplane. In cases where the data is not linearly separable, SVM uses kernel functions (e.g., polynomial or radial basis function) to map the data into a higher-dimensional space where a linear separation is possible. By solving an optimization problem, SVM ensures that the best hyperplane is chosen, providing robust classification with good generalization to unseen data.
Requirements for implementation:
Nonlinear SVM: Implementing kernel functions to map data to higher dimensions.
Quadratic Programming (QP): The optimization problem SVM needs to solve is a convex QP problem. DAPHNE will need an efficient QP solver for this step.
Linear Algebra Libraries: DAPHNE likely integrates with linear algebra libraries optimized for its backend. It could be leveraged for matrix multiplications, dot products, and more.
Sparse Data Support: DAPHNE is designed for high-performance scenarios, so handling sparse datasets efficiently will be key to ensuring that the SVM scales to large datasets.
DAPHNE handles large-scale matrix and tensor operations. DAPHNE's optimized routines can be used to handle matrix multiplications, transpositions, and dot products.
SVM also relies on vector norms and dot products, which can be efficiently implemented using DAPHNE’s underlying
To search for the best parameters like C and gamma, a strategy like Grid Search, Random Search, or Bayesian Optimization, paired with cross-validation is needed to avoid overfitting.
FAmirjani
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3 days ago
Support Vector Machines (SVM) is a supervised machine learning algorithm for classification and regression tasks. SVM works by finding the optimal hyperplane that separates data points of different classes in a high-dimensional space. The goal is to maximize the margin, which is the distance between the closest data points (called support vectors) from each class and the separating hyperplane. In cases where the data is not linearly separable, SVM uses kernel functions (e.g., polynomial or radial basis function) to map the data into a higher-dimensional space where a linear separation is possible. By solving an optimization problem, SVM ensures that the best hyperplane is chosen, providing robust classification with good generalization to unseen data. Requirements for implementation: