Perceptrons: expanded edition
Connectionism and cognitive architecture: a critical analysis
Connections and symbols
What size net gives valid generalization?
Neural Computation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Modern database systems
Are Multilayer Perceptrons Adequate for Pattern Recognition and Verification?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Strip trees: a hierarchical representation for curves
Communications of the ACM
Region representation: boundary codes from quadtrees
Communications of the ACM
Integration of Graphical Rules with Adaptive Learning of Structured Information
Hybrid Neural Systems, revised papers from a workshop
Efficient computation and data structures for graphics.
Efficient computation and data structures for graphics.
Stochastic Error-Correcting Syntax Analysis for Recognition of Noisy Patterns
IEEE Transactions on Computers
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
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This paper proposes a general framework for the development of a novel approach to pattern recognition which is strongly based on graphical data types. These data keep at the same time the highly structured representation of classical syntactic and structural approaches and the subsymbolic capabilities of decision-theoretic approaches, typical of connectionist and statistical models. Like for decision-theoretic models, the recognition ability is mainly gained on the basis of learning from examples, that, however, are strongly structured.