Support Vector Machines (SVM) are supervised learning models used for classification and regression tasks, known for their ability to find the optimal hyperplane that maximizes the margin between different classes in a dataset. They are particularly effective in high-dimensional spaces and are robust to overfitting, especially in cases where the number of dimensions exceeds the number of samples.

Support Vector Machines

supervised learning models

Classification tasks

regression tasks

optimal hyperplane

maximizes the margin

different classes

High-dimensional spaces refer to mathematical spaces with a large number of dimensions, often used in fields like machine learning and data analysis to represent complex data structures. These spaces pose unique challenges such as the 'curse of dimensionality,' which can lead to increased computational complexity and data sparsity, affecting the performance of algorithms.

high-dimensional spaces

robust to overfitting

number of dimensions

number of samples

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