Evaluating the Vapnik-Chervonenkis dimension of artificial neural networks using the Poincare polynomial (vol 12, pg 403, 1999)

被引：0

作者：

Carter, MA

Oxley, ME

机构：

[1] USAF, Inst Technol, Grad Sch Engn & Management, Dept Math & Stat, Wright Patterson AFB, OH 45433 USA

[2] Natl Air Intelligence Ctr, Wright Patterson AFB, OH 45433 USA

来源：

NEURAL NETWORKS | 2001年 / 14卷 / 10期

关键词：

All Open Access; Bronze;

D O I：

10.1016/S0893-6080(01)00125-3

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

引用

页码：1469 / 1470

页数：2

共 46 条

[1] Evaluating the Vapnik-Chervonenkis dimension of artificial neural networks using the Poincare polynomial
Carter, MA
Oxley, ME
NEURAL NETWORKS, 1999, 12 (03) : 403 - 408
[2] Evaluating the Vapnik-Chervonenkis dimension of artificial neural networks using the Poincare polynomial
Oxley, ME
Carter, MA
APPLICATIONS AND SCIENCE OF COMPUTATIONAL INTELLIGENCE II, 1999, 3722 : 68 - 75
[3] Evaluating the Vapnik-Chervonenkis dimension of artificial neural networks using the Poincare polynomial
National Air Intelligence Center, Wright-Patterson AFB, 4115 Hebble Creek Rd., Dayton, OH 45433-5622, United States
不详
Neural Netw., 3 (403-408):
[4] Vapnik-Chervonenkis dimension of recurrent neural networks
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Sontag, ED
DISCRETE APPLIED MATHEMATICS, 1998, 86 (01) : 63 - 79
[5] The Vapnik-Chervonenkis dimension of graph and recursive neural networks
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[6] Vapnik-Chervonenkis dimension of neural networks with binary weights
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Engel, A
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[7] On using the Poincare polynomial for calculating the VC dimension of neural networks
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NEURAL NETWORKS, 2001, 14 (10) : 1465 - 1465
[8] Response: on using the Poincare polynomial for calculating the V-C dimension of neural networks
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Oxley, ME
NEURAL NETWORKS, 2001, 14 (10) : 1467 - 1467
[9] Modelling primary production in a coastal embayment affected upwelling using dynamic ecosystem models and artificial neural networks (vol 120, pg 1999, 1999)
Barciela, RM
Garcia, E
Fernández, E
ECOLOGICAL MODELLING, 2000, 128 (2-3) : 251 - 251
[10] Spectroscopic quantification of bacteria using artificial neural networks (vol 67, pg 2550, 2004)
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JOURNAL OF FOOD PROTECTION, 2004, 67 (12) : 2642 - 2642

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