Multithreaded local learning regularization neural networks for regression tasks

Abstract

We explore four local learning versions of regularization networks. While global learning algorithms create a global model for all testing points, the local learning algorithms use neighborhoods to learn local parameters and create on the fly a local model specifically designed for any particular testing point. This approach delivers breakthrough performance in many application domains. Usually however the computational overhead is substantial, and in some cases prohibited. For speeding up the online predictions we exploit both multithreaded parallel implementations as well as interplay between locally optimized parameters and globally optimized parameters. The multithreaded local learning regularization neural networks are implemented with OpenMP. The accuracy of the algorithms is tested against several benchmark datasets. The parallel efficiency and speedup is evaluated on a multi-core system.