# Supervised neural networks for the classification of structures

@article{Sperduti1997SupervisedNN, title={Supervised neural networks for the classification of structures}, author={Alessandro Sperduti and Antonina Starita}, journal={IEEE transactions on neural networks}, year={1997}, volume={8 3}, pages={ 714-35 } }

Standard neural networks and statistical methods are usually believed to be inadequate when dealing with complex structures because of their feature-based approach. [... ] Key Method By using generalized recursive neurons, all the supervised networks developed for the classification of sequences, such as backpropagation through time networks, real-time recurrent networks, simple recurrent networks, recurrent cascade correlation networks, and neural trees can, on the whole, be generalized to structures. Theā¦ Expand

## 480 Citations

Neural Networks for Processing Data Structures

- Computer ScienceSummer School on Neural Networks
- 1997

It is not difficult to figure out how to extract tree automata from a neural network for structures, and this would allow the above scheme to work on the other side around, with a neural module which is driven by a symbolic subsystem.

A General Framework for Self-Organizing Structure Processing Neural Networks

- Computer Science
- 2003

A general recursive dynamic is defined which enables the recursive processing of complex data structures based on recursively computed internal representations of the respective context and allows the transfer of theoretical issues from the SOM literature to the structure processing case.

Artificial Neural Network Models

- Computer ScienceHandbook of Computational Intelligence
- 2015

The main models and developments in the broad field of artificial neural networks (ANN) are outlined, including biological neurons motivates the initial formal neuron model ā the perceptron, and the basic principles of training the corresponding ANN models on an appropriate data collection are outlined.

Entropy-based generation of supervised neural networks for classification of structured patterns

- Computer ScienceIEEE Transactions on Neural Networks
- 2004

An entropy-based approach for constructing such neural networks for classification of acyclic structured patterns and results have shown that the networks constructed by this method can have a better performance, with respect to network size, learning speed, or recognition accuracy, than the networks obtained by other methods.

Theoretical properties of recursive neural networks with linear neurons

- Computer ScienceIEEE Trans. Neural Networks
- 2001

Some theoretical results about linear recursive neural networks are presented that allow one to establish conditions on their dynamical properties and their capability to encode and classify structured information.

A Simple and Effective Neural Model for the Classification of Structured Patterns

- Computer ScienceKES
- 2007

The idea is to describe a graph as an algebraic relation, i.e. as a subset of the Cartesian product, and the class-posterior probabilities given the relation are reduced to products of probabilistic quantities estimated using a multilayer perceptron.

A general framework for adaptive processing of data structures

- Computer ScienceIEEE Trans. Neural Networks
- 1998

The framework described in this paper is an attempt to unify adaptive models like artificial neural nets and belief nets for the problem of processing structured information, where relations between data variables are expressed by directed acyclic graphs, where both numerical and categorical values coexist.

Inductive Learning in Symbolic Domains Using Structure-Driven Recurrent Neural Networks

- Computer ScienceKI
- 1996

A connectionist architecture together with a novel supervised learning scheme which is capable of solving inductive inference tasks on complex symbolic structures of arbitrary size and first results from experiments with inductive learning tasks consisting in the classification of logical terms are given.

Learning Efficiently with Neural Networks: A Theoretical Comparison between Structured and Flat Representations

- Computer ScienceECAI
- 2000

The message of this paper is that, whenever structured representations are available, they should be preferred to "flat" (array based) representations because they are likely to simplify learning in terms of time complexity.

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