Pages that link to "Item:Q4701158"
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The following pages link to The minimax distortion redundancy in empirical quantizer design (Q4701158):
Displaying 18 items.
- Fast rates for empirical vector quantization (Q351685) (← links)
- A statistical view of clustering performance through the theory of \(U\)-processes (Q392047) (← links)
- Vector quantization and clustering in the presence of censoring (Q495381) (← links)
- Empirical risk minimization for heavy-tailed losses (Q892246) (← links)
- A notion of stability for \(k\)-means clustering (Q1711576) (← links)
- A quasi-Bayesian perspective to online clustering (Q1786586) (← links)
- On Hölder fields clustering (Q1936547) (← links)
- Robust \(k\)-means clustering for distributions with two moments (Q2054489) (← links)
- On strong consistency of kernel \(k\)-means: a Rademacher complexity approach (Q2070586) (← links)
- A \(k\)-points-based distance for robust geometric inference (Q2203630) (← links)
- Nonasymptotic bounds for vector quantization in Hilbert spaces (Q2343956) (← links)
- A framework for statistical clustering with constant time approximation algorithms for \(K\)-median and \(K\)-means clustering (Q2384132) (← links)
- Consistency of spectral clustering (Q2426615) (← links)
- Statistical learning guarantees for compressive clustering and compressive mixture modeling (Q2664825) (← links)
- Convergence of the $k$-Means Minimization Problem using $\Gamma$-Convergence (Q3452592) (← links)
- Learning Finite-Dimensional Coding Schemes with Nonlinear Reconstruction Maps (Q5025792) (← links)
- Dimensionality-Dependent Generalization Bounds for <i>k</i>-Dimensional Coding Schemes (Q5380581) (← links)
- Learning Theory (Q5473635) (← links)
