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Implementation of Perceptron for Classification

version 1.0.0 (2.44 KB) by RFM
Implementing Perceptron in Matlab from Scratch without using the Built-in Functions.


Updated 30 May 2020

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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 (2022). Implementation of Perceptron for Classification (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2018b
Compatible with any release
Platform Compatibility
Windows macOS Linux

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