Support Vector Machine for Language Classification Implemented in MATLAB

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Resource Overview

A MATLAB-based support vector machine implementation designed for language classification, featuring efficient text processing and multi-class recognition capabilities

Detailed Documentation

This resource presents a MATLAB-implemented language classifier based on Support Vector Machines (SVM). The classifier is capable of performing classification and recognition tasks across different languages. The implementation utilizes MATLAB's built-in SVM functions from the Statistics and Machine Learning Toolbox, particularly fitcsvm for binary classification and fitcecoc for multi-class language differentiation. Key features include text preprocessing routines for tokenization and feature extraction, kernel function optimization for handling high-dimensional language data, and cross-validation mechanisms to ensure model robustness. The system employs TF-IDF (Term Frequency-Inverse Document Frequency) vectorization to convert textual input into numerical features suitable for SVM processing, with customizable parameters for kernel selection (linear, RBF, polynomial) and regularization tuning to optimize classification accuracy across diverse linguistic datasets.

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