MATLAB Source Code for Relevance Vector Machine (RVM)

This resource provides complete MATLAB source code for Relevance Vector Machine (RVM) implementation, featuring optimized fast algorithms and detailed usage instructions. The code implements RVM as a sparse probabilistic model using the same functional framework as Support Vector Machines, designed for predictive and classification tasks. The implementation includes efficient parameter estimation routines and kernel function handling. Key advantages of this RVM implementation: (1) The algorithm outputs not only point estimates but also complete predictive distributions for target variables, providing comprehensive uncertainty quantification through probabilistic inference methods. (2) The implementation utilizes significantly fewer relevance vectors compared to support vectors in SVM, achieved through automatic relevance determination (ARD) techniques that dramatically reduce computational complexity during prediction phases. (3) The RVM framework requires estimation of fewer hyperparameters than comparable methods, with the code implementing efficient type-II maximum likelihood estimation for simplified model configuration. (4) The kernel function implementation has no restrictions regarding Mercer's theorem conditions, allowing flexible use of various kernel types including non-Mercer kernels, with modular kernel selection functionality in the codebase. This enhanced source code provides robust RVM implementation with additional functionality and optimization features, making predictive and classification tasks more computationally efficient and adaptable to various problem domains. The code includes comprehensive error handling, data preprocessing modules, and visualization tools for result analysis.