Steen, E.W. van der (2003) Effects of Noise in Train Data for Neural Classifiers. Master's Thesis / Essay, Computing Science.
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Abstract
When training neural networks with real-life data, there is always some form of noise involved. There are many types of noise, and the effects they have on neural networks vary greatly. Some noise causes patterns to be incorrectly labelled; other forms can garble the input values. In neural applications, injecting noise into the inputs of training-data can improve performance, for it may enhance the generalization ability of neural networks. In contrast, it became apparent in recent studies, that reducing noise, like removing patterns of occluded images from the training data, actually improved performance. When new data is obtained, often much pre-processing is done to verify whether the data is suitable for training. Noise is one of the causes that can reduce the learnability of a data set, and time and effort is often spent to deal with it in some way. In this thesis we will analyse effects of noise in data used for training neural classifiers, with the purpose of estimating whether pre-processing is necessary or not.
| Item Type: | Thesis (Master's Thesis / Essay) |
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| Degree programme: | Computing Science |
| Thesis type: | Master's Thesis / Essay |
| Language: | English |
| Date Deposited: | 15 Feb 2018 07:29 |
| Last Modified: | 15 Feb 2018 07:29 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/8871 |
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