Pages that link to "Item:Q2030496"
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The following pages link to A novel embedded min-max approach for feature selection in nonlinear support vector machine classification (Q2030496):
Displaying 18 items.
- Budget constrained non-monotonic feature selection (Q1669185) (← links)
- Concept-cognitive computing system for dynamic classification (Q2140166) (← links)
- Preference estimation under bounded rationality: identification of attribute non-attendance in stated-choice data using a support vector machines approach (Q2171627) (← links)
- Sparse optimization in feature selection: application in neuroimaging (Q2250090) (← links)
- Embedded feature-selection support vector machine for driving pattern recognition (Q2263641) (← links)
- Maximum relevance minimum common redundancy feature selection for nonlinear data (Q2293268) (← links)
- Primal explicit max margin feature selection for nonlinear support vector machines (Q2629859) (← links)
- Optimal feature selection for support vector machines (Q2654221) (← links)
- A feature selection method for nonparallel plane support vector machine classification (Q2885489) (← links)
- On Minimax Fractional Semi-Infinite Programming Problems with Applications (Q5861981) (← links)
- Combined SVM-based feature selection and classification (Q5896777) (← links)
- Combined SVM-based feature selection and classification (Q5920547) (← links)
- Margin optimal classification trees (Q6065640) (← links)
- A neurodynamic optimization approach to supervised feature selection via fractional programming (Q6078682) (← links)
- On clustering and interpreting with rules by means of mathematical optimization (Q6109555) (← links)
- Dendrograms, minimum spanning trees and feature selection (Q6167525) (← links)
- Kent feature embedding for classification of compositional data with zeros (Q6494411) (← links)
- A distributionally robust chance-constrained kernel-free quadratic surface support vector machine (Q6586255) (← links)
