Implementation of Perceptron for Classification
Implementing Perceptron in Matlab from Scratch without using the Built-in Functions.
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Steps included:-
1. Read Data and Divide into Training and Testing Data
2. Perform Perceptron Training till all training samples are correctly classified
3. Perform Testing using the Final Updated Weights
4. Plot Decision Boundary on scatter plot
5. Check performance through Confusion Matrix
Cite As
RFM (2026). Implementation of Perceptron for Classification (https://www.mathworks.com/matlabcentral/fileexchange/76431-implementation-of-perceptron-for-classification), MATLAB Central File Exchange. Retrieved .
General Information
- Version 1.0.0 (2.44 KB)
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 1.0.0 |
