Relevance Vector Machine Toolbox Versions 1 and 2

The Relevance Vector Machine (RVM) is a Bayesian framework-based sparse probabilistic model widely used for regression and classification tasks. Its key advantage lies in automatic feature relevance selection and generation of sparser models compared to traditional methods.

First Edition Toolbox Features: Implements core RVM functionality for both regression and classification using Bayesian inference algorithms. Includes straightforward demo scripts with data preprocessing and model training pipelines. Optimized for smaller datasets with efficient matrix operations and memory management.

Second Edition Toolbox Enhancements: Expands kernel function library with configurable hyperparameters through improved optimization routines. Implements large-scale data handling via chunking algorithms and parallel computing capabilities. Adds model evaluation modules including k-fold cross-validation, error metric calculations, and diagnostic plotting functions. Provides advanced demonstration cases covering real-world scenarios with comprehensive parameter tuning examples.

Both toolbox versions are designed for machine learning practitioners, enabling rapid model validation through executable demo scripts for various regression forecasting and classification applications.