Discrete neural computation: a theoretical foundation
Discrete neural computation: a theoretical foundation
Extended Cascade-Correlation for Syntactic and Structural Pattern Recognition
SSPR '96 Proceedings of the 6th International Workshop on Advances in Structural and Syntactical Pattern Recognition
A learning algorithm for continually running fully recurrent neural networks
Neural Computation
Learning distributed representations for the classification of terms
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Supervised neural networks for the classification of structures
IEEE Transactions on Neural Networks
A general framework for adaptive processing of data structures
IEEE Transactions on Neural Networks
Theoretical properties of recursive neural networks with linear neurons
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Linear separability of sequences and structured data is studied. On the basis of a theoretical model, necessary and sufficient conditions for nonlinear separability are derived by a well known result for vectors. Examples of sufficient conditions for linear separability of both sequences and structured data are given.