Visscher, R. (1997) Minimal High Order Percepron Construction. Master's Thesis / Essay, Artificial Intelligence.
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Abstract
High Order Perceptrons offer an elegant solution to the problem of finding the amount of hidden layers in multilayer perceptrons. High order perceptrons only have an input and an output layer, whose size is completely defined by the problem to be solved. The major drawback of high order perceptrons is the exponential number of possible connections, which can even become infinite. The aim of this work is to find ways of restricting the amount of connections by verifying a restriction method on the order of the network and to identify a heuristic which can be used in an ontogenic method for the dynamical construction of the connectivity of the high order perceptron. Besides these two issues an answer is also found to whether rerandomization of the parameters is beneficial for the construction. Keywords: ontogenic neural networks, pruning, generalization, high order perceptrons, partially connected networks, backpropagation neural networks, feature selection.
| Item Type: | Thesis (Master's Thesis / Essay) |
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| Degree programme: | Artificial Intelligence |
| Thesis type: | Master's Thesis / Essay |
| Language: | English |
| Date Deposited: | 15 Feb 2018 07:30 |
| Last Modified: | 15 Feb 2018 07:30 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/9020 |
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