Discretized-Vapnik-Chervonenkis dimension for analyzing complexity of real function classes
In this paper, we introduce the discretized-Vapnik-Chervonenkis (VC) dimension for studying the complexity of a real function class, and then analyze properties of real function classes and neural networks. We first prove that a countable traversal set is enough to achieve the VC dimension for a rea...
| Main Authors: | , , , |
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| 其他作者: | |
| 格式: | Journal Article |
| 語言: | English |
| 出版: |
2013
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/99545 http://hdl.handle.net/10220/13524 |