A Novel Ensemble Approach for Improving Generalization Ability of Neural Networks

2008 ◽  
pp. 164-171
Author(s):  
Lei Lu ◽  
Xiaoqin Zeng ◽  
Shengli Wu ◽  
Shuiming Zhong
Keyword(s):  
Neural Networks ◽  
Generalization Ability ◽  
Ensemble Approach

Ensemble approach for improving generalization ability of neural networks

2013 ◽  
Author(s):  
Shaib Ahmed ◽  
Md. Razibul Islam Razib ◽  
Mohammed Shamsul Alam ◽  
Mohammad Shafiul Alam ◽  
Mohammad Nurul Huda
Keyword(s):  
Neural Networks ◽  
Generalization Ability ◽  
Ensemble Approach

COMBINING REGRESSION ESTIMATORS: GA-BASED SELECTIVE NEURAL NETWORK ENSEMBLE

2001 ◽  
Vol 01 (04) ◽  
pp. 341-356 ◽  
Author(s):  
ZHI-HUA ZHOU ◽  
JIAN-XIN WU ◽  
WEI TANG ◽  
ZHAO-QIAN CHEN
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computational Cost ◽  
Neural Network Ensemble ◽  
Generalization Ability ◽  
Ensemble Approach ◽  
The Neural Network ◽  
Random Weights ◽  
Regression Estimators ◽  
The Individual

Neural network ensemble is a learning paradigm where a collection of neural networks is trained for the same task. In this paper, the relationship between the generalization ability of the neural network ensemble and the correlation of the individual neural networks constituting the ensemble is analyzed in the context of combining neural regression estimators, which reveals that ensembling a selective subset of trained networks is superior to ensembling all the trained networks in some cases. Based on such recognition, an approach named GASEN is proposed. GASEN trains a number of individual neural networks at first. Then it assigns random weights to the individual networks and employs a genetic algorithm to evolve those weights so that they can characterize to some extent the importance of the individual networks in constituting an ensemble. Finally it selects an optimum subset of individual networks based on the evolved weights to make up the ensemble. Experimental results show that, comparing with a popular ensemble approach, i.e., averaging all, and a theoretically optimum selective ensemble approach, i.e. enumerating, GASEN has preferable performance in generating ensembles with strong generalization ability in relatively small computational cost. This paper also analyzes the working mechanism of GASEN from the view of error-ambiguity decomposition, which reveals that GASEN improves generalization ability mainly through reducing the average generalization error of the individual neural networks constituting the ensemble.

scholarly journals A Study for Keeping Generalization Ability of Multilayered Neural Networks using Evolution Strategies

1997 ◽  
Vol 117 (2) ◽  
pp. 143-149
Author(s):  
Satoru Ogawa ◽  
Takao Watanebe ◽  
Keiichiro Yasuda ◽  
Ryuichi Yokoyama
Keyword(s):  
Neural Networks ◽  
Evolution Strategies ◽  
Generalization Ability

scholarly journals TEXT SENTIMENT ANALYSIS BASED ON CNNS AND SVM

2019 ◽  
Vol 7 (6) ◽  
pp. 77-83 ◽  
Author(s):  
Dr. C. Arunabala ◽  
P. Jwalitha ◽  
Soniya Nuthalapati
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Sentiment Analysis ◽  
Expressive Power ◽  
Sentiment Classification ◽  
Experimental Results ◽  
Analysis Method ◽  
Mapping Functions ◽  
Generalization Ability ◽  
Text Sentiment Analysis

The traditional text sentiment analysis method is mainly based on machine learning. However, its dependence on emotion dictionary construction and artificial design and extraction features makes the generalization ability limited. In contrast, depth models have more powerful expressive power, and can learn complex mapping functions from data to affective semantics better. In this paper, a Convolution Neural Networks (CNNs) model combined with SVM text sentiment analysis is proposed. The experimental results show that the proposed method improves the accuracy of text sentiment classification effectively compared with traditional CNN, and confirms the effectiveness of sentiment analysis based on CNNs and SVM

A data ensemble approach for real-time air quality forecasting using extremely randomized trees and deep neural networks

2019 ◽  
Vol 32 (11) ◽  
pp. 7563-7579 ◽  
Author(s):  
Ebrahim Eslami ◽  
Ahmed Khan Salman ◽  
Yunsoo Choi ◽  
Alqamah Sayeed ◽  
Yannic Lops
Keyword(s):  
Neural Networks ◽  
Air Quality ◽  
Real Time ◽  
Deep Neural Networks ◽  
Extremely Randomized Trees ◽  
Ensemble Approach ◽  
Air Quality Forecasting

Error Analysis of Neural Network with Multiply Neurons Using Rational Spline Weight Functions

2015 ◽  
Vol 713-715 ◽  
pp. 1716-1720
Author(s):  
Dai Yuan Zhang ◽  
Lei Lei Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Error Analysis ◽  
Weight Functions ◽  
Generalization Ability ◽  
Rational Spline ◽  
Error Formula ◽  
Denominator Polynomial

In order to describe the generalization ability, this paper discusses the error analysis of neural network with multiply neurons using rational spline weight functions. We use the cubic numerator polynomial and linear denominator polynomial as the rational splines for weight functions. We derive the error formula for approximation, the results can be used to algorithms for training neural networks.

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