The VC dimension of metric balls under Fréchet and Hausdorff distances
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Publication:2665263
DOI10.1007/s00454-021-00318-zOpenAlexW3191148826MaRDI QIDQ2665263
André Nusser, Anne Driemel, Jeff M. Phillips, Ioannis Psarros
Publication date: 18 November 2021
Published in: Discrete \& Computational Geometry (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1903.03211
General topics of discrete mathematics in relation to computer science (68R01) Computer graphics; computational geometry (digital and algorithmic aspects) (68U05) General topics in the theory of data (68P01)
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Cites Work
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- A new upper bound for the VC-dimension of visibility regions
- VC dimensions of principal component analysis
- Relative \((p,\varepsilon )\)-approximations in geometry
- Polynomial bounds for VC dimension of sigmoidal and general Pfaffian neural networks
- \(\epsilon\)-nets and simplex range queries
- Guarding galleries where no point sees a small area.
- Quasi-optimal range searching in spaces of finite VC-dimension
- Bounding the Vapnik-Chervonenkis dimension of concept classes parameterized by real numbers
- Almost optimal set covers in finite VC-dimension
- Approximate matching of polygonal shapes
- Fast Fréchet queries
- FRESH: Fréchet similarity with hashing
- Fast algorithms for approximate Fréchet matching queries in geometric trees
- A combinatorial problem; stability and order for models and theories in infinitary languages
- On the density of families of sets
- Learnability and the Vapnik-Chervonenkis dimension
- Clustering time series under the Fréchet distance
- COMPUTING THE FRÉCHET DISTANCE BETWEEN TWO POLYGONAL CURVES
- Neural Network Learning
- Walking the Dog Fast in Practice: Algorithm Engineering of the Fréchet Distance
- Straight-Path Queries in Trajectory Data
- Comparing distributions and shapes using the kernel distance
- On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
- Improved bounds on the sample complexity of learning
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