Iterative least squares functional networks classifier.
This paper proposes unconstrained functional networks as a new classifier to deal with the pattern recognition problems. Both methodology and learning algorithm for this kind of computational intelligence classifier using the iterative least squares optimization criterion are derived. The performance of this new intelligent systems scheme is demonstrated and examined using real-world applications. A comparative study with the most common classification algorithms in both machine learning and statistics communities is carried out. The study was achieved with only sets of second-order linearly independent polynomial functions to approximate the neuron functions. The results show that this new framework classifier is reliable, flexible, stable, and achieves a high-quality performance.
El-Sebakhy
Emad A
EA
Computer Science, College of Computer Science and Engineering, Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dahran 31261, Saudi Arabia. dodi5@ccse.kfupm.edu.sa
Hadi
Ali S
AS
Faisal
Kanaan A
KA
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003661:Decision Support Techniques; D016247:Information Storage and Retrieval / Q000379:methods*; D016018:Least-Squares Analysis; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*