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ACSC
2008
IEEE
15 years 7 months ago
An investigation of the state formation and transition limitations for prediction problems in recurrent neural networks
Recurrent neural networks are able to store information about previous as well as current inputs. This "memory" allows them to solve temporal problems such as language r...
Angel Kennedy, Cara MacNish
112
Voted
NECO
2010
75views more  NECO 2010»
14 years 11 months ago
How to Modify a Neural Network Gradually Without Changing Its Input-Output Functionality
Christopher DiMattina, Kechen Zhang
180
Voted
ACG
2003
Springer
15 years 10 months ago
Evaluation in Go by a Neural Network using Soft Segmentation
In this article a neural network architecture is presented that is able to build a soft segmentation of a two-dimensional input. This network architecture is applied to position ev...
Markus Enzenberger
ICANN
2001
Springer
15 years 9 months ago
Product Unit Neural Networks with Constant Depth and Superlinear VC Dimension
Abstract. It has remained an open question whether there exist product unit networks with constant depth that have superlinear VC dimension. In this paper we give an answer by cons...
Michael Schmitt