Abstract
For a general infinite distance space X, with no assumptions about the distance function, which need not satisfy the metric axioms, it is not clear what the VC-dimension of the class H of half-spaces in X may be and if there are generalization error bounds for learning H. We define a combinatorial dimension of X to be the independence number of the class of balls in X. We compute it for Euclidean space and for several non-metric distance spaces. Using this dimension, we are able to provide a generalization error bound for learning H over any infinite distance space X.
Original language | English |
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Article number | 105008 |
Journal | Information and Computation |
Volume | 291 |
DOIs | |
State | Published - Mar 2023 |
Keywords
- Distance space
- Dual VC-dimension
- Independence number
- Learning with large margin (width)
- Non-metric space
- Pseudo-dimension
- Regular n-simplex
- VC-dimension