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Support Vector Machine Calculator

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

Inputs

Results

Estimated Support Vectors

100

Margin Width

2

Training Complexity1,000,000
Prediction Complexity1,000
Effective Dimensionality10
Regularization Strength1
How to Use This Calculator
  1. Enter the number of training samples and feature dimensions.
  2. Select the kernel type (linear, RBF, polynomial) based on data separability.
  3. Input the regularization parameter C and kernel-specific parameters (gamma for RBF, degree for polynomial).
  4. Review the estimated support vector count and decision boundary margin.
  5. Use cross-validation to tune C and gamma — a grid search over log-scale values is standard practice.

How the result changes with Number of Samples

Number of SamplesEstimated Support VectorsMargin Width
100,00910,0012
350,00735,0012
650,00465,0002
900,00190,0002

What each input means

C Parameter
Regularization parameter
Gamma (RBF)
RBF kernel parameter
Kernel Type
SVM kernel type
Polynomial Degree
Degree for polynomial kernel
Number of Features
Input feature dimensions
Number of Samples
Training samples

How this is calculated

Worked example, using the default values

  1. Identify Input Parameters
    4 parameters
    C Parameter = 1, Gamma (RBF) = 0.1, Kernel Type = 0, Polynomial Degree = 3 = 6 input(s) provided
  2. Calculate Estimated Support Vectors
    Estimated Support Vectors
    100 = 100
  3. Calculate Margin Width
    Margin Width
    2 = 2
  4. Calculate Training Complexity
    Training Complexity
    1000000 = 1000000
  5. Calculate Prediction Complexity
    Prediction Complexity
    1000 = 1000

Engine last updated . Checked against 2 independently-derived tests — how we verify calculators.

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