top
logo

Login Form



Visitors Counter

mod_vvisit_counterToday37
mod_vvisit_counterYesterday52
mod_vvisit_counterThis week338
mod_vvisit_counterThis month1537
mod_vvisit_counterAll155508

Who's Online

We have 28 guests online

Home Members Ioannis E. Livieris Forecasting economy-related data utilizing constrained recurrent neural networks
Error
  • Error loading feed data.
  • Error loading feed data.
Forecasting economy-related data utilizing constrained recurrent neural networks PDF Print E-mail

I.E. Livieris. Forecasting economy-related data utilizing constrained recurrent neural networks. Algorithms, (accepted) 2019.

 

 

Abstract - During the last decades, machine learning constitutes a significant tool in extracting useful knowledge from economic data for assisting decision-making. In this work, we evaluate the performance of weight-constrained recurrent neural networks in forecasting economic classification problems. These networks are efficiently trained with a recently proposed training algorithm, which has two major advantages. Firstly, it exploits the numerical efficiency and very low memory requirements of the limited memory BFGS matrices; Secondly, it utilizes a gradient-projection strategy for handling the bounds on the weights. The reported numerical experiments present the classification accuracy of the proposed model, providing empirical evidence that the application of the bounds on the weights of the recurrent neural network, provides more stable and reliable learning.

 

Search Engines




bottom
top

Department of Mathematics

Educational Software News

Call for papers

Newest Education Titles


bottom

Designed by Ioannis E. Livieris. | Validate XHTML | CSS