Neural Network Studies. 3. Variable Selection in the Cascade-Correlation Learning Architecture

1998 ◽  
Vol 38 (4) ◽  
pp. 651-659 ◽  
Author(s):  
Vasyl V. Kovalishyn ◽  
Igor V. Tetko ◽  
Alexander I. Luik ◽  
Vladyslav V. Kholodovych ◽  
Alessandro E. P. Villa ◽  
...  
Keyword(s):  
Neural Network ◽  
Variable Selection ◽  
Cascade Correlation ◽  
Correlation Learning ◽  
Network Studies

Neural Network Studies. 2. Variable Selection

1996 ◽  
Vol 36 (4) ◽  
pp. 794-803 ◽  
Author(s):  
Igor V. Tetko ◽  
Alessandro E. P. Villa ◽  
David J. Livingstone
Keyword(s):  
Neural Network ◽  
Variable Selection ◽  
Network Studies

Variable Selection in the Cascade-Correlation Learning Architecture

2000 ◽  
pp. 472-473 ◽  
Author(s):  
Igor V. Tetko ◽  
Vasyl V. Kovalishyn ◽  
Alexander I. Luik ◽  
Tamara N. Kasheva ◽  
Alessandro E. P. Villa ◽  
...  
Keyword(s):  
Variable Selection ◽  
Cascade Correlation ◽  
Correlation Learning

Neural Network Studies

1993 ◽  
Author(s):  
Gregg Wilensky ◽  
Narbik Manukian ◽  
Joseph Neuhaus ◽  
Natalie Rivetti
Keyword(s):  
Neural Network ◽  
Network Studies

Predicted Number of Pregnant Women in Aichi Prefecture, Japan: Estimation by Machine Learning Database Construction for Disaster Preparation

2021 ◽  
pp. 1-9
Author(s):  
Kanetoshi Hattori ◽  
Ritsuko Hattori
Keyword(s):  
Machine Learning ◽  
Pregnant Women ◽  
Disaster Preparedness ◽  
Correlation Coefficients ◽  
Database Construction ◽  
Aichi Prefecture ◽  
Cascade Correlation ◽  
Correlation Learning ◽  
Disaster Preparation ◽  
Area Data

Abstract Aichi prefecture, Japan is predicted to be hit by Mega-earthquake. Aichi Prefectural Association of Midwives has been making efforts to improve disaster preparedness for pregnant women. This project aims to acquire area data of pregnant women for simulated studies of rescue activities. Number of women in census survey areas in Nagoya City was acquired from nationwide data of pregnant women by machine learning (Cascade-Correlation Learning Architecture). Quite high correlation coefficients between actual data and estimation data were observed. Rescue simulations have been carried out based on the data acquired by this study.

A Novel Quantum Neural Network Model with Variable Selection for Short Term Load Forecasting

2010 ◽  
Vol 20-23 ◽  
pp. 612-617 ◽  
Author(s):  
Wei Sun ◽  
Yu Jun He ◽  
Ming Meng
Keyword(s):  
Neural Network ◽  
Variable Selection ◽  
Network Model ◽  
Neural Network Model ◽  
Load Forecasting ◽  
Short Term ◽  
Bp Network ◽  
Quantum Neural Network ◽  
Short Term Load Forecasting ◽  
Selection For

The paper presents a novel quantum neural network (QNN) model with variable selection for short term load forecasting. In the proposed QNN model, first, the combiniation of maximum conditonal entropy theory and principal component analysis method is used to select main influential factors with maximum correlation degree to power load index, thus getting effective input variables set. Then the quantum neural network forecating model is constructed. The proposed QNN forecastig model is tested for certain province load data. The experiments and the performance with QNN neural network model are given, and the results showed the method could provide a satisfactory improvement of the forecasting accuracy compared with traditional BP network model.

Sign in / Sign up

Export Citation Format

Share Document