Application of Cascade Correlation Networks for Structures toChemistry
Applied Intelligence
Simple Strategies to Encode Tree Automata in Sigmoid Recursive Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Generalization Ability of Folding Networks
IEEE Transactions on Knowledge and Data Engineering
Logo Recognition by Recursive Neural Networks
GREC '97 Selected Papers from the Second International Workshop on Graphics Recognition, Algorithms and Systems
Towards Incremental Parsing of Natural Language Using Recursive Neural Networks
Applied Intelligence
On the efficient classification of data structures by neural networks
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
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
On the implementation of frontier-to-root tree automata in recursive neural networks
IEEE Transactions on Neural Networks
Processing acyclic data structures using modified self-organizing maps
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Recurrent networks for structured data - A unifying approach and its properties
Cognitive Systems Research
Self-Organizing Hidden Markov Model Map (SOHMMM)
Neural Networks
| Hi-index | 0.00 |
Structured domains are characterized by complex patterns which are usually represented as lists, trees, and graphs of variable sizes and complexity. The ability to recognize and classify these patterns is fundamental for several applications that use, generate or manipulate structures. In this paper I review some of the concepts underpinning Recursive Neural Networks, i.e. neural network models able to deal with data represented as directed acyclic graphs.