東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Applying neural networking technique...

Grauer, Eyal.

Linked to FindBook

Google Book

Amazon

博客來

Applying neural networking techniques to improve performance and turnover prediction.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applying neural networking techniques to improve performance and turnover prediction./
Author:	Grauer, Eyal.
Description:	134 p.
Notes:	Source: Dissertation Abstracts International, Volume: 67-10, Section: B, page: 6103.
Contained By:	Dissertation Abstracts International67-10B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3237821
ISBN:	9780542928420

Applying neural networking techniques to improve performance and turnover prediction.
Grauer, Eyal.

Applying neural networking techniques to improve performance and turnover prediction. - 134 p.

Source: Dissertation Abstracts International, Volume: 67-10, Section: B, page: 6103.

Thesis (Ph.D.)--Bowling Green State University, 2006.

Neural networking techniques were compared with standard regression techniques in a selection context. Neural networking models were hypothesized to outperform standard regression techniques in predicting turnover and six objective job performance metrics using a standard pre-employment assessment battery. Seven inputs, representing all independent variables, and seven outputs, representing the dependent variables, were assessed. A field sample (N = 1632) from the telecommunications industry was used. For regression models, ordinary least squares and logistic regression were used. A wide range of neural network models were tested. All neural network models examined in this study were supervised feedforward models with backpropagation of error. The number of hidden units in the neural networks were varied from 1 to 14. Three learning parameters of the networks were examined: .1, .2, and .3. Epochs of 100, 500, 1000, and 5000 were run on ten sub-samples, leading to a total of 1680 neural network analyses. Results indicated that neither regression techniques nor neural networking techniques consistently predicted turnover or job performance.

ISBN: 9780542928420Subjects--Topical Terms:

517247
Statistics.

Applying neural networking techniques to improve performance and turnover prediction.
LDR:02107nmm 2200301 4500 001 1834427
005 20071119145648.5
008 130610s2006 eng d
020 $a 9780542928420
035 $a (UMI)AAI3237821
035 $a AAI3237821
040 $a UMI $c UMI
100 1 $a Grauer, Eyal. $3 1923079
245 1 0 $a Applying neural networking techniques to improve performance and turnover prediction.
300 $a 134 p.
500 $a Source: Dissertation Abstracts International, Volume: 67-10, Section: B, page: 6103.
500 $a Adviser: Michael J. Zickar.
502 $a Thesis (Ph.D.)--Bowling Green State University, 2006.
520 $a Neural networking techniques were compared with standard regression techniques in a selection context. Neural networking models were hypothesized to outperform standard regression techniques in predicting turnover and six objective job performance metrics using a standard pre-employment assessment battery. Seven inputs, representing all independent variables, and seven outputs, representing the dependent variables, were assessed. A field sample (N = 1632) from the telecommunications industry was used. For regression models, ordinary least squares and logistic regression were used. A wide range of neural network models were tested. All neural network models examined in this study were supervised feedforward models with backpropagation of error. The number of hidden units in the neural networks were varied from 1 to 14. Three learning parameters of the networks were examined: .1, .2, and .3. Epochs of 100, 500, 1000, and 5000 were run on ten sub-samples, leading to a total of 1680 neural network analyses. Results indicated that neither regression techniques nor neural networking techniques consistently predicted turnover or job performance.
590 $a School code: 0018.
650 4 $a Statistics. $3 517247
650 4 $a Psychology, Industrial. $3 520063
650 4 $a Psychology, Personality. $3 1017585
650 4 $a Artificial Intelligence. $3 769149
690 $a 0463
690 $a 0624
690 $a 0625
690 $a 0800
710 2 0 $a Bowling Green State University. $3 676285
773 0 $t Dissertation Abstracts International $g 67-10B.
790 1 0 $a Zickar, Michael J., $e advisor
790 $a 0018
791 $a Ph.D.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3237821