Week 5 (June 24 – June 30) Progress

This week, I worked on ClassificationPartitionedModel for the ClassificationSVM crossval method.

I used the following resources:

  • Libsvm PDF by CSIE: This document contains the mathematical formulations implemented in LIBSVM, which I will use to cross-check against MATLAB's documentation.
  • README provided with the LIBSVM package for MATLAB/Octave.
  • The ClassificationPartitionedModel.m file by Ruchika.

Comments

Post a Comment

Popular posts from this blog

Wrapping Up My GSoC Journey: A Look Back at the Project and What’s Next

As my Google Summer of Code (GSoC) 2024 journey comes to an end, I find myself reflecting on everything I’ve learned, the challenges I’ve faced, and the milestones I’ve achieved. This experience has been a whirlwind of coding, debugging, and learning—one that I’ll carry with me for a long time. What Was the Project All About? For those who might not know, my project focused on extending the GNU Octave Statistics package by adding two significant class definitions: ClassificationSVM and ClassificationNeuralNetwork . These additions are designed to enhance the machine-learning capabilities of Octave, providing users with tools to classify data using Support Vector Machines (SVM) and Neural Networks. You can check out the full details of what was accomplished, the challenges I faced, and the current state of the project in my final report here . The Journey When I started this project, I was both excited and nervous. Working on an established open-source platform like Octave was a dream,...
Read more

Week 8 (July 15 –July 21) Progress

Started work on backpropagation for ClassificationNeuralNetwork class After reviewing the  MATLAB documentation , I successfully implemented the following components for the ClassificationNeuralNetwork class : Weights Initializer: Layer weights are initialized using either 'glorot' or 'he'. (Helper function) Biases Initializer: Biases are initialized using either 'zeros' or 'ones'. (Helper function) Loss Function: Cross entropy. (Helper function) One-Hot Vector Encoder: (Helper function) I ran various tests to ensure everything is working correctly, such as: Cross Entropy Loss Function: I created matrices for y_pred and y_act and fed them to an online function and to my implementation. The outputs matched. Forward Propagation: I used trained weights and biases from MATLAB for the Fisheriris dataset, gave them to my predict function, and the outputs were the same. The only remaining task is backpropagation, and I am facing some issues like non-co...
Read more