Pages that link to "Item:Q890097"

The following pages link to Global binary optimization on graphs for classification of high-dimensional data (Q890097):

Displaying 14 items.

• Convex variational methods on graphs for multiclass segmentation of high-dimensional data and point clouds (Q1702637) (← links)
• An effective region force for some variational models for learning and clustering (Q1703055) (← links)
• Graph clustering, variational image segmentation methods and Hough transform scale detection for object measurement in images (Q2014485) (← links)
• Stochastic block models are a discrete surface tension (Q2022738) (← links)
• Semisupervised data classification via the Mumford-Shah-Potts-type model (Q2294163) (← links)
• Comparisons of different methods for balanced data classification under the discrete non-local total variational framework (Q2668555) (← links)
• Diffuse interface models on graphs for classification of high dimensional data (Q2805269) (← links)
• Simplified Energy Landscape for Modularity Using Total Variation (Q4686633) (← links)
• Theoretical Analysis of Flows Estimating Eigenfunctions of One-Homogeneous Functionals (Q4689766) (← links)
• Modified Cheeger and ratio cut methods using the Ginzburg–Landau functional for classification of high-dimensional data (Q5348005) (← links)
• Preconditioned Algorithm for Difference of Convex Functions with Applications to Graph Ginzburg–Landau Model (Q6088330) (← links)
• An efficient and versatile variational method for high-dimensional data classification (Q6604513) (← links)
• The Potts model with different piecewise constant representations and fast algorithms: a survey (Q6606497) (← links)
• Double-well net for image segmentation (Q6669799) (← links)