Vstupní data a jejich význam pro vrstevnaté neuronové sítě
| Thesis title in Czech: | Vstupní data a jejich význam pro vrstevnaté neuronové sítě |
|---|---|
| Thesis title in English: | Input data and their significance for multi-layered feed-forward neural networks |
| Academic year of topic announcement: | 2007/2008 |
| Thesis type: | diploma thesis |
| Thesis language: | angličtina |
| Department: | Department of Software Engineering (32-KSI) |
| Supervisor: | doc. RNDr. Iveta Mrázová, CSc. |
| Author: | hidden - assigned and confirmed by the Study Dept. |
| Date of registration: | 01.11.2007 |
| Date of assignment: | 01.11.2007 |
| Date and time of defence: | 13.09.2010 00:00 |
| Date of electronic submission: | 13.09.2010 |
| Date of proceeded defence: | 13.09.2010 |
| Opponents: | RNDr. Jiří Iša |
| Guidelines |
| The student shall review the following topics in the diploma thesis:
- overview and comparison of various paradigms applicable to learning of multi-layered neural networks (the backpropagation algorithm, conjugate gradients, learning with hints etc.) - recapitulation and mutual comparison of known techniques suitable for the estimation of the significance of presented patterns and their features for training and recall of a given network (e.g. enforced internal representation, sensitivity analysis, models based on the principles of self-organization, etc.) - interpretation and visualization of the detected dependencies - e.g. of outliers and the assigned semantics The student will focus on some of these topics in more detail. Further, he will propose a suitable strategy for data pre-processing based on real-world data and he shall implement the models. The evaluation of the obtained results and gained experience shall form an important part of the thesis. |
| References |
| 1. Některé z dostupných základních učebnic vhodných pro zvolené téma,
např.: - M. Berry, G. Linoff: Data Mining Techniques For Marketing, Sales, and Customer Support, John Wiley & Sons, 1997 - R. Rojas: Neural Networks: A Systematic Introduction, Springer- Verlag, 1996 - S. Haykin: Neural Networks: A Comprehensive Foundation, Prentice Hall, Upper Saddle River, N. J., 1999 2. Články: - S. G. Pierce, Y. Ben-Haim, K. Worden, G. Manson: Evaluation of neural network robust reliability using information-gap theory, in: IEEE Transactions on Neural Networks, Vol. 17, No. 6, (2006), pp. 1349-1361. - X. Zeng, D. S. Yeung: Sensitivity analysis of multilayer perceptron to input and weight perturbations, in: IEEE Transactions on Neural Networks, Vol. 12, No. 6, (2001), pp. 1358-1366. - F. Flentge: Locally weighted interpolating growing neural gas, in: IEEE Transactions on Neural Networks, Vol. 17, No. 6, (2006), pp. 1382-1393. - I. Mrázová, D. Wang: Improved generalization of neural classifiers with enforced internal representation, in: Neurocomputing, Vol. 70, (2007), pp. 2940-2952. - N. Karayiannis, Y. Xiong: Training reformulated radial basis function neural networks capable of identifying uncertainty in data classification, in: IEEE Transactions on Neural Networks, Vol. 17, No. 5, (2006), pp. 1222-1234. 3. Aktuální články z profilujících světových časopisů, např.: Neurocomputing, Neural |