Pages that link to "Item:Q1944318"
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The following pages link to An approximation theory approach to learning with \(\ell^1\) regularization (Q1944318):
Displaying 20 items.
- Sharp learning rates of coefficient-based \(l^q\)-regularized regression with indefinite kernels (Q370936) (← links)
- An empirical feature-based learning algorithm producing sparse approximations (Q413648) (← links)
- Learning by nonsymmetric kernels with data dependent spaces and \(\ell^1\)-regularizer (Q543025) (← links)
- Concentration estimates for learning with \(\ell ^{1}\)-regularizer and data dependent hypothesis spaces (Q550498) (← links)
- Regularized learning in Banach spaces as an optimization problem: representer theorems (Q693135) (← links)
- Learning theory approach to a system identification problem involving atomic norm (Q895425) (← links)
- A simpler approach to coefficient regularized support vector machines regression (Q1722337) (← links)
- Support vector machines regression with \(l^1\)-regularizer (Q1759352) (← links)
- Learning rates for \(l^1\)-regularized kernel classifiers (Q1789959) (← links)
- Stability analysis of learning algorithms for ontology similarity computation (Q2016684) (← links)
- On empirical eigenfunction-based ranking with \(\ell^1\) norm regularization (Q2256621) (← links)
- Approximation methods for supervised learning (Q2433154) (← links)
- Reproducing Kernel Banach Spaces with the ℓ<sup>1</sup> Norm II: Error Analysis for Regularized Least Square Regression (Q3116949) (← links)
- Learning by atomic norm regularization with polynomial kernels (Q3451221) (← links)
- (Q4969089) (← links)
- Multikernel Regression with Sparsity Constraint (Q4999353) (← links)
- (Q5214255) (← links)
- AN ERROR ANALYSIS OF LAVRENTIEV REGULARIZATION IN LEARNING THEORY (Q5324151) (← links)
- Learning with Convex Loss and Indefinite Kernels (Q5378314) (← links)
- (Q5419932) (← links)
