Structured domains axe 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.
Neural Networks for Adaptive Processing of Data Structures
SPERDUTI, ALESSANDRO
2001
Abstract
Structured domains axe 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.
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