14 citations · 29 across the 3 of their papers we have counts for
4 papers
Deep Incremental Boosting
Alan Mosca, George D Magoulas
This paper introduces Deep Incremental Boosting, a new technique derived from AdaBoost, specifically adapted to work with Deep Learning methods, that reduces the required training…
Adapting Resilient Propagation for Deep Learning
Alan Mosca, George D. Magoulas
The Resilient Propagation (Rprop) algorithm has been very popular for backpropagation training of multilayer feed-forward neural networks in various applications. The standard Rpro…
Transfer learning approach for financial applications
Cosmin Stamate, George D. Magoulas, Michael S. C. Thomas
Artificial neural networks learn how to solve new problems through a computationally intense and time consuming process. One way to reduce the amount of time required is to inject…
Evolving Stochastic Learning Algorithm Based on Tsallis Entropic Index
Aristoklis D. Anastasiadis, George D. Magoulas
In this paper, inspired from our previous algorithm, which was based on the theory of Tsallis statistical mechanics, we develop a new evolving stochastic learning algorithm for neu…