A Collection of RBF Source Code Implementations

This document compiles several source programs implementing RBF (Radial Basis Function) algorithms, specifically curated for beginners in machine learning. RBF is a fundamental algorithm widely used in pattern recognition and machine learning applications. The core mechanism involves mapping input data to a high-dimensional feature space using radial basis functions for classification and prediction tasks. The provided source codes demonstrate practical implementations of RBF networks, typically including: - Kernel function computation using Gaussian or other radial basis functions - Center selection algorithms (random selection or k-means clustering) - Weight calculation methods through linear regression or pseudo-inverse techniques - Training procedures for parameter optimization These programs help beginners understand RBF's architecture through hands-on coding experience. By studying the source code and conducting practical experiments, learners can deepen their comprehension of how RBF transforms input patterns, selects optimal centers, and computes network weights. The implementations also illustrate how to apply RBF to solve real-world problems like classification, function approximation, and time-series prediction. Each program includes commented code sections explaining key functions such as: 1. Radial basis kernel calculations with adjustable spread parameters 2. Training algorithms for determining hidden layer parameters 3. Prediction functions for new data inference 4. Performance evaluation metrics for model validation We hope these practical code examples provide valuable learning resources for beginners to master RBF algorithm implementation and applications!