Gaussian Process Algorithms for Regression and Classification Applications

Gaussian Process algorithms demonstrate extensive applications in both regression and classification domains. As a statistically principled machine learning methodology, they enable robust prediction and classification across diverse data types. The accompanying program for the book "Gaussian Process-Based Machine Learning" presents the latest v3.1 release, updated on September 27, 2010. This version incorporates significant enhancements including optimized hyperparameter tuning procedures, expanded covariance kernel selections (e.g., RBF, Matern), and efficient inference algorithms for marginal likelihood computation. The implementation leverages Cholesky decomposition for numerical stability during matrix inversion and supports both exact and sparse approximation methods for scalability. Whether for beginners exploring probabilistic machine learning or professionals developing advanced models, this toolkit provides practical hands-on experience with Gaussian Process applications through well-documented code structures and modular function design. The updated release aims to deliver greater utility and inspiration through its refined algorithmic implementations and comprehensive demonstration cases.