Support Vector Machine Calculator

Calculate SVM parameters, support vectors, margin width, and complexity for support vector machines.

C Parameter

Gamma (RBF)

Kernel Type

Polynomial Degree

Number of Features

Number of Samples

Estimated Support Vectors

100

Margin Width

Training Complexity1,000,000

Prediction Complexity1,000

Effective Dimensionality10

Regularization Strength1

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Formula

Margin = 2 / ||w||

Calculate accuracy, precision, recall, F1 score, MCC, AUC, and other classification performance metrics.

Calculate optimization convergence, computational cost, efficiency, and memory requirements for ML optimizers.

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