Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis
The Journal of Machine Learning Research
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
A Parameter-Free Classification Method for Large Scale Learning
The Journal of Machine Learning Research
An incremental learning method for neural networks based on sensitivity analysis
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
Performance evaluation of microbial fuel cell by artificial intelligence methods
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Until recently, the most common criterion in machine learning for evaluating the performance of algorithms was accuracy. However, the unrestrainable growth of the volume of data in recent years in fields such as bioinformatics, intrusion detection or engineering, has raised new challenges in machine learning not simply regarding accuracy but also scalability. In this research, we are concerned with the scalability of one of the most well-known paradigms in machine learning, artificial neural networks (ANNs), particularly with the training algorithm Sensitivity-Based Linear Learning Method (SBLLM). SBLLM is a learning method for two-layer feedforward ANNs based on sensitivity analysis, that calculates the weights by solving a linear system of equations. The results show that the training algorithm SBLLM performs better in terms of scalability than five of the most popular and efficient training algorithms for ANNs.