On the Complexity of Recognizing Regions Computable by Two-Layered Perceptrons
| Type of publication: | Journal paper |
| Citation: | Mayo98a1 |
| Journal: | Annals Mathematics and Artificial Intelligence |
| Year: | 1999 |
| Crossref: | mayo98a: |
| Abstract: | This work is concerned with the computational complexity of the recognition of $ÞPtwo$, the class of regions of the Euclidian space that can be classified exactly by a two-layered perceptron. Some subclasses of $ÞPtwo$ of particular interest are also studied, such as the class of iterated differences of polyhedra, or the class of regions $V$ that can be classified by a two-layered perceptron with as only hidden units the ones associated to $(d-1)$-dimensional facets of $V$. In this paper, we show that the recognition problem for $ÞPtwo$ as well as most other subclasses considered here is \NPH\ in the most general case. We then identify special cases that admit polynomial time algorithms. |
| Userfields: | ipdinar={1998}, ipdmembership={learning}, |
| Keywords: | |
| Projects |
Idiap |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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