Name
Title | ||
|---|---|---|
A Comparison between the Linear Neural Network Method and the Multiple Linear Regression Method in the Modeling of Continuous Data. |
Abstract | ||
|---|---|---|
Both linear neural network and multiple linear regression models can be used for multi-factor analysis and forecasting, but the data of the multiple linear regression model are required to meet such conditions as independence and normality, while the data of the linear neural network are only required to have a linear relationship. This article uses the same set of data to establish respectively a linear neural network model and a multiple linear regression model, compares the abilities of fitting and forecasting of the two kinds of models, and consequently, comes to the conclusion that the linear neural network method has a stronger fitting ability and a more stable ability of prediction so that it can be further applied and promoted in the analyzing and forecasting of continuous data factors. © 2011 ACADEMY PUBLISHER. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.4304/jcp.6.10.2143-2148 | JCP |
Keywords | Field | DocType |
comparison,continuous data,fitting,forecasting,hospital charge,linear neural network,modeling,multiple linear regression,relative error | Principal component regression,Linear model,General linear model,Computer science,Proper linear model,Artificial intelligence,Log-linear model,Linear predictor function,Generalized linear mixed model,Machine learning,Linear regression | Journal |
Volume | Issue | Citations |
6 | 10 | 1 |
PageRank | References | Authors |
0.36 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guoli Wang | 1 | 2 | 2.41 |
| Jianhui Wu | 2 | 1 | 1.37 |
| Jianhua Wu | 3 | 16 | 4.04 |
| Xiaohong Wang | 4 | 2 | 3.09 |