RBFMIP: A Package for Training Multi-Instance RBF Neural Networks with Advanced Learning Capabilities

RBFMIP is a comprehensive software package for training multi-instance Radial Basis Function neural networks. The package provides a powerful toolkit that enables users to train RBF neural network models using multi-instance learning methodologies. Through RBFMIP, users can effectively process and leverage multi-instance datasets, significantly enhancing model accuracy and performance metrics. The implementation includes specialized algorithms for instance bag processing, centroid calculation for RBF centers, and adaptive weight optimization between instances. Key functions handle instance relationship modeling through distance metrics and kernel functions, while supporting various RBF activation functions like Gaussian and multiquadric. Additionally, RBFMIP offers flexible configuration options and an intuitive interface, allowing users to effortlessly conduct model training and parameter tuning. The package supports batch processing of instance bags, automatic center selection algorithms, and cross-validation mechanisms for optimal model performance. Whether you're a researcher or practitioner, RBFMIP serves as an invaluable tool for achieving superior results in the multi-instance learning domain, with capabilities for handling complex data structures and producing interpretable model outputs.