The following pages link to Learning Theory (Q4680894):
Displaying 12 items.
- A statistical view of clustering performance through the theory of \(U\)-processes (Q392047) (← links)
- A fast and recursive algorithm for clustering large datasets with \(k\)-medians (Q434902) (← links)
- Optimal time bounds for approximate clustering (Q703075) (← links)
- A \(k\)-median algorithm with running time independent of data size (Q703077) (← links)
- Metric \(k\)-median clustering in insertion-only streams (Q2231758) (← 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)
- Sublinear time approximate clustering (Q2768331) (← links)
- Approximation Algorithms for Aversion k-Clustering via Local k-Median (Q4598205) (← links)
- Automata, Languages and Programming (Q5466479) (← links)
- Automata, Languages and Programming (Q5716761) (← links)
- Also for \(k\)-means: more data does not imply better performance (Q6134361) (← links)
