https://github.com/aryehky/arduino

🚀 C++ Machine Learning Project: Digit Recognition with Support Vector Machine (SVM) 🖥️ This project is a robust implementation of digit recognition using Support Vector Machine (SVM) in C++. The SVM algorithm, a powerful supervised learning technique, is employed to classify handwritten digits from the famous MNIST dataset.
https://github.com/aryehky/arduino

cpp data-visualization machine-learning preprocessing

Last synced: 28 days ago
JSON representation

🚀 C++ Machine Learning Project: Digit Recognition with Support Vector Machine (SVM) 🖥️ This project is a robust implementation of digit recognition using Support Vector Machine (SVM) in C++. The SVM algorithm, a powerful supervised learning technique, is employed to classify handwritten digits from the famous MNIST dataset.

• Host: GitHub
• URL: https://github.com/aryehky/arduino
• Owner: aryehky
• Created: 2023-10-01T20:36:35.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2024-10-24T12:43:30.000Z (2 months ago)
• Last Synced: 2024-10-24T17:07:14.540Z (2 months ago)
• Topics: cpp, data-visualization, machine-learning, preprocessing
• Language: C++
• Homepage:
• Size: 86.9 KB
• Stars: 3
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# C++ Machine Learning Project: Digit Recognition with Support Vector Machine (SVM)

## Overview

This project aims to implement a digit recognition system using Support Vector Machine (SVM) in C++. SVM is a powerful supervised learning algorithm used for classification tasks.

## Features

- **Dataset:** Utilize the MNIST dataset for training and testing.

- **Preprocessing:** Implement preprocessing steps such as normalization and feature extraction.

- **SVM Implementation:** Develop SVM classifier using libraries like LIBSVM or implement from scratch.

- **Training:** Train the SVM model on the training dataset.

- **Testing:** Evaluate the model's accuracy on the test dataset.

- **Prediction:** Implement a function to predict digits based on input images.

- **Performance Metrics:** Calculate and display metrics like accuracy, precision, and recall.

- **User Interface (Optional):** Develop a simple CLI or GUI for interacting with the model.

## Technology Stack

- **Language:** C++

- **Libraries:** LIBSVM (or similar for SVM implementation)

- **Dataset:** MNIST dataset (or similar digit recognition dataset)

- **Development Tools:** IDE like Visual Studio or Code::Blocks

## Implementation Steps

1. **Dataset Preparation:** Download and preprocess the MNIST dataset.

2. **SVM Model Development:** Implement SVM classifier using chosen library or custom implementation.

3. **Training:** Train the SVM model on the training dataset.

4. **Testing and Evaluation:** Test the model on the test dataset and calculate performance metrics.

5. **Prediction Function:** Implement a function to predict digits based on user input.

6. **User Interface (Optional):** Develop a simple interface for easy interaction with the model.

## Example Code Snippet (SVM Training)

```cpp

// Example using LIBSVM for SVM training

#include 

#include "svm.h"

int main() {

    // Load training data

    svm_problem prob;

    // Initialize prob with your training data (features and labels)

    // Set SVM parameters

    svm_parameter param;

    svm_set_default_parameter(&param);

    param.svm_type = C_SVC;

    param.kernel_type = RBF;

    param.gamma = 0.5;

    // Train SVM model

    svm_model *model = svm_train(&prob, &param);

    // Save model for future use

    svm_save_model("svm_model.model", model);

    // Free memory

    svm_free_and_destroy_model(&model);

    svm_destroy_param(&param);

    return 0;

}

.

.

.

.