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
Estimated Support Vectors
100
Margin Width
2
How to Use This Calculator
- Start by filling in the input fields below. Results update instantly as you type, so you can experiment with different values to see how they affect the outcome.
- C Parameter — Regularization parameter Accepts values from 0.001 to 1,000 (default: 1).
- Gamma (RBF) — RBF kernel parameter Accepts values from 0.001 to 10 (default: 0.1).
- Kernel Type — SVM kernel type Choose from: Linear, RBF (Radial Basis Function), Polynomial. default: 0.
- Polynomial Degree — Degree for polynomial kernel Accepts values from 2 to 10 (default: 3).
- Number of Features — Input feature dimensions Accepts values from 1 to 10,000 (default: 10).
- Number of Samples — Training samples Accepts values from 10 to 1,000,000 (default: 1,000).
- Once all inputs are set, review your results in the Results panel. Here's what each output means:
- Estimated Support Vectors — shown as a numeric value. This is the primary result of this calculator.
- Margin Width — shown as a numeric value. This is the primary result of this calculator.
- Training Complexity — shown as a numeric value.
- Prediction Complexity — shown as a numeric value.
- Effective Dimensionality — shown as a numeric value.
- Regularization Strength — shown as a numeric value.
- Explore the related calculators below if you need deeper analysis or want to approach this topic from a different angle.
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Formula
Margin = 2 / ||w||Related Calculators
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