Pages that link to "Item:Q1566102"
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The following pages link to Constructing ultrametric and additive trees based on the \(L_1\) norm. (Q1566102):
Displaying 10 items.
- Least squares algorithms for constructing constrained ultrametric and additive tree representations of symmetric proximity data (Q1113590) (← links)
- Trees, not cubes: Hypercontractivity, cosiness, and noise stability (Q1298338) (← links)
- Optimal variable weighting for ultrametric and additive trees and \(K\)-means partitioning: Methods and software. (Q1566106) (← links)
- \(L_1\) optimization under linear inequality constraints (Q1566115) (← links)
- Data model and classification by trees; The minimum variance reduction (MVR) method (Q1584597) (← links)
- An algorithm to obtain additive trees based upon the preservation of the clusters structure (Q1874090) (← links)
- Deriving ultrametric tree structures from proximity data confounded by differential stimulus familiarity (Q1901365) (← links)
- A parametric procedure for ultrametric tree estimation from conditional rank order proximity data (Q1901372) (← links)
- Iterative projection strategies for the least‐squares fitting of tree structures to proximity data (Q4715685) (← links)
- Average Consensus and Infinite Norm Consensus : Two Methods for Ultrametric Trees (Q5302521) (← links)
