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Support Vector Machine (SVM) is a supervised machine learning algorithm used primarily for classification and regression tasks. It works by finding the hyperplane that best separates data points from different classes with the maximum margin.
SVM operates by transforming data into a higher-dimensional space to find the optimal decision boundary between classes. It uses support vectors—data points closest to the boundary—to define this hyperplane. SVM can handle linear and non-linear problems using kernel functions like polynomial, RBF (radial basis function), and sigmoid kernels.
It is highly effective in high-dimensional spaces and when the number of dimensions exceeds the number of samples. SVM is also memory efficient as it uses only a subset of training points in the decision function.
While SVMs can be computationally intensive with large datasets, they are known for their accuracy, especially in binary classification problems such as image recognition, text categorization, and bioinformatics.
SVM identifies the optimal hyperplane that maximizes the margin between different class labels using support vectors.
A kernel function transforms input data into a higher-dimensional space to make it linearly separable.
SVMs can be computationally expensive on very large datasets, but they are effective for smaller to medium-sized datasets with high dimensionality.
Applications include handwriting recognition, speech classification, cancer diagnosis, and bioinformatics.
SVM is based on maximizing the margin between classes using hyperplanes, while decision trees split data based on feature conditions.
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