From Continuous Behaviour to Discrete Knowledge
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Neural networks have proven to be very powerful techniques for solving a wide range of tasks. However, the learned concepts are unreadable for humans. Some works try to obtain symbolic models from the networks, once these networks have been trained, allowing to understand the model by means of decision trees or rules that are closer to human understanding. The main problem of this approach is that neural networks output a continuous range of values, so even though a symbolic technique could be used to work with continuous classes, this output would still be hard to understand for humans. In this work, we present a system that is able to model a neural network behaviour by discretizing its outputs with a vector quantization approach, allowing to apply the symbolic method.
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- From Continuous Behaviour to Discrete Knowledge
- Book Title
- Artificial Neural Nets Problem Solving Methods
- Book Subtitle
- 7th International Work-Conference on Artificial and Natural Neural Networks, IWANN2003 Maó, Menorca, Spain, June 3–6, 2003 Proceedings, Part II
- pp 217-224
- Print ISBN
- Online ISBN
- Series Title
- Lecture Notes in Computer Science
- Series Volume
- Series ISSN
- Springer Berlin Heidelberg
- Copyright Holder
- Springer Berlin Heidelberg
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- Editor Affiliations
- 4. E.T.S. de Ingeniería Informática Departamento de Inteligencia Artificial, Universidad Nacional de Educación a Distancia
- Author Affiliations
- 5. Universidad Carlos III de Madrid, Avda. de la Universidad, 30, 28911, Leganés, Madrid, Spain
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