Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)最新文献_第6页

Financial forecasting and rules extraction from trained networks 从训练好的网络中进行财务预测和规则提取

Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94) Pub Date : 1994-06-27 DOI: 10.1109/ICNN.1994.374745

R. Kane, N. Milgram

引用次数: 13

Structure adaptation in feed-forward neural networks 前馈神经网络的结构自适应

Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94) Pub Date : 1994-06-27 DOI: 10.1109/ICNN.1994.374491

K. Khorasani, W. Weng

引用次数: 10

Comparing artificial neural networks to other statistical methods for medical outcome prediction 比较人工神经网络与其他统计方法在医疗结果预测中的应用

Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94) Pub Date : 1994-06-27 DOI: 10.1109/ICNN.1994.374560

H. Burke, D. B. Rosen, P. Goodman

{"title":"Comparing artificial neural networks to other statistical methods for medical outcome prediction","authors":"H. Burke, D. B. Rosen, P. Goodman","doi":"10.1109/ICNN.1994.374560","DOIUrl":"https://doi.org/10.1109/ICNN.1994.374560","url":null,"abstract":"Survival prediction is important in cancer because it determines therapy, matches patients for clinical trials, and provides patient information. Is a backpropagation neural network more accurate at predicting survival in breast cancer than the current staging system? For over thirty years cancer outcome prediction has been based on the pTNM staging system. There are two problems with this system: (1) it is not very accurate, and (2) its accuracy can not be improved because predictive variables can not be added to the model without increasing the model's complexity to the point where it is no longer useful to the clinician. Using the area under the curve (AUC) of the receiver operating characteristic, the authors compare the accuracy of the following predictive models: pTNM stage, principal components analysis, classification and regression trees, logistic regression, cascade correlation neural network, conjugate gradient descent neural network, backpropagation neural network, and probabilistic neural network. Using just the TNM variables both the backpropagation neural network, AUC.768, and the probabilistic neural network, AUC.759, are significantly more accurate than the pTNM stage system, AUC.720 (all SEs<.01, p<.01 for both models compared to the pTNM model). Adding variables further increases the prediction accuracy of the backpropagation neural network, AUC.779, and the probabilistic neural network, AUC.777. Adding the new prognostic factors p53 and HER-2/neu increases the backpropagation neural network's accuracy to an AUC of .850. The neural networks perform equally well when applied to another breast cancer data set and to a colorectal cancer data set. Neural networks are able to significantly improve breast cancer outcome prediction accuracy when compared to the TNM stage system. They can combine prognostic factors to further improve accuracy. Neural networks are robust across data bases and cancer sites. Neural networks can perform as well as the best traditional prediction methods, and they can capture the power of nonmonotonic predictors and discover complex genetic interactions.<<ETX>>","PeriodicalId":209128,"journal":{"name":"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124767909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 43

A Lyapunov machine for stability analysis of nonlinear systems 非线性系统稳定性分析的李雅普诺夫机

Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94) Pub Date : 1994-06-27 DOI: 10.1109/ICNN.1994.374324

D. V. Prokhorov

引用次数: 18

Recurrent neural networks and Fibonacci numeration system of order s(s/spl ges/2) 递归神经网络与s(s/spl ges/2)阶Fibonacci计数系统

Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94) Pub Date : 1994-06-27 DOI: 10.1109/ICNN.1994.374510

M. Yacoub

引用次数: 0

Pattern theory for character recognition 字符识别的模式理论

Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94) Pub Date : 1994-06-27 DOI: 10.1109/ICNN.1994.374939

J. Jean, K. Xue, S. Goel

引用次数: 1

Learning and tuning fuzzy logic controllers through genetic algorithm 利用遗传算法对模糊控制器进行学习和整定

Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94) Pub Date : 1994-06-27 DOI: 10.1109/ICNN.1994.374400

Shuqing Zeng, Yongbao He

引用次数: 17

A neural network based real-time robot tracking controller using position sensitive detectors 一种基于位置敏感检测器的神经网络机器人实时跟踪控制器

Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94) Pub Date : 1994-06-27 DOI: 10.1109/ICNN.1994.374666

Hyoung‐Gweon Park, Se-Young Oh

引用次数: 5

High capacity for the Hopfield neural networks Hopfield神经网络的高容量

Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94) Pub Date : 1994-06-27 DOI: 10.1109/ICNN.1994.374349

Chang-Jiu Chen, J. Cheung, A. Haque

引用次数: 1

Illusory contour detection using MRF models 磁共振成像模型的虚幻轮廓检测

Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94) Pub Date : 1994-06-27 DOI: 10.1109/ICNN.1994.374966

S. Madarasmi, T. Pong, D. Kersten

引用次数: 7