## Experimental Evaluation of Generalization
Capability of Incremental Projection Learning in Neural Networks

*Hendri Murfi*

`hendri@makara.cso.ui.ac.id`

Mathematics

**University of Indonesia**

Indonesia

### Abstract

One of the essences of learning in neural network is generalization
capability. It is an ability to give an accurate result for data that was not
learned in learning process. One of learning method that has goal to increase
generalization capability directly is incremental projection learning. This learning
method use analysis functional approach in reproducing kernel Hilbert space. This paper
will describe experimental evaluation of generalization capability of incremental projection
learning. Then, Make comparison with other learning method, i.e. incremental back
propagation learning and incremental radial basis learning. Base on our experiment,
incremental projection learning gives better generalization capability when the number
of training examples is small and the noise variance is large. The other case, it does
not always give the better generalization capability. Even though, In case learning data
are big enough and noise variance are small enough, incremental projection learning always
gives worse generalization capability than incremental back propagation learning.
Our experiment used Matlab.
Key Words: generalization capability, Incremental projection learning, neural network

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