Machine learning methods that can handle variable-size structured data such as sequences and graphs include Bayesian networks (BNs) and Recursive Neural Networks (RNNs). In both classes of models, the data is modeled using a set of observed and hidden variables associated with the nodes of a directe
On some relationships between hierarchies of quasiarithmetic means and neural networks
✍ Scribed by Vicenç Torra
- Publisher
- John Wiley and Sons
- Year
- 1999
- Tongue
- English
- Weight
- 125 KB
- Volume
- 14
- Category
- Article
- ISSN
- 0884-8173
No coin nor oath required. For personal study only.
✦ Synopsis
In this work, we establish the relations between neural networks and hierarchies of quasiarithmetic means. We show that a neural network with the same activation function in all the neurons gives an output that is isomorphic to the result that can be obtained with a hierarchy of quasiarithmetic means. From this result, we show that hierarchies of quasiarithmetic means are universal approximations.
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