Pages that link to "Item:Q2505653"

The following pages link to Learning rates of least-square regularized regression (Q2505653):

Displaying 50 items.

• Convergence analysis of coefficient-based regularization under moment incremental condition (Q2874064) (← links)
• Error analysis on regularized learning (Q2886129) (← links)
• Least-squares regularized regression with dependent samples and<i>q</i>-penalty (Q2903163) (← links)
• ERM learning algorithm for multi-class classification (Q2909368) (← links)
• The coefficient regularized regression with random projection (Q2911899) (← links)
• The convergence rate of learning algorithms for least square regression with sample dependent hypothesis spaces (Q2916787) (← links)
• Learning rates of Tikhonov regularized regressions based on sample dependent RKHS (Q2917676) (← links)
• Regression learning with non-identically and non-independently sampling (Q2958504) (← links)
• Optimal rate of the regularized regression learning algorithm (Q3008355) (← links)
• Least Square Regression with <i>l^p</i>-Coefficient Regularization (Q3067078) (← links)
• Error analysis of multicategory support vector machine classifiers (Q3071817) (← links)
• GENERALIZATION BOUNDS OF REGULARIZATION ALGORITHMS DERIVED SIMULTANEOUSLY THROUGH HYPOTHESIS SPACE COMPLEXITY, ALGORITHMIC STABILITY AND DATA QUALITY (Q3087503) (← links)
• Reproducing Kernel Banach Spaces with the ℓ¹ Norm II: Error Analysis for Regularized Least Square Regression (Q3116949) (← links)
• Estimates of learning rates of regularized regression via polyline functions (Q3118875) (← links)
• Approximate Minimization of the Regularized Expected Error over Kernel Models (Q3168992) (← links)
• Analysis of Regression Algorithms with Unbounded Sampling (Q3386411) (← links)
• Learning by atomic norm regularization with polynomial kernels (Q3451221) (← links)
• Spectral Algorithms for Supervised Learning (Q3510946) (← links)
• The consistency of least-square regularized regression with negative association sequence (Q4564912) (← links)
• Regularized learning schemes in feature Banach spaces (Q4594821) (← links)
• (Q4637006) (← links)
• (Q4637042) (← links)
• Simultaneous estimations of optimal directions and optimal transformations for functional data (Q4689136) (← links)
• REGULARIZED LEAST SQUARE ALGORITHM WITH TWO KERNELS (Q4917257) (← links)
• Error analysis of the kernel regularized regression based on refined convex losses and RKBSs (Q5022936) (← links)
• Error analysis of the moving least-squares regression learning algorithm with <i>β</i>-mixing and non-identical sampling (Q5030625) (← links)
• Error analysis of the moving least-squares method with non-identical sampling (Q5031809) (← links)
• Generalization and learning rate of multi-class support vector classification and regression (Q5097891) (← links)
• On the K-functional in learning theory (Q5107666) (← links)
• (Q5148925) (← links)
• Coefficient-based regularization network with variance loss for error (Q5150110) (← links)
• (Q5159408) (← links)
• (Q5159455) (← links)
• Learning Rates of <i>l^q</i> Coefficient Regularization Learning with Gaussian Kernel (Q5175497) (← links)
• REGULARIZED LEAST SQUARE REGRESSION WITH SPHERICAL POLYNOMIAL KERNELS (Q5189976) (← links)
• LEARNING RATES OF REGULARIZED REGRESSION FOR FUNCTIONAL DATA (Q5189981) (← links)
• (Q5214255) (← links)
• Deep neural networks for rotation-invariance approximation and learning (Q5236745) (← links)
• Optimal rate for support vector machine regression with Markov chain samples (Q5248169) (← links)
• Regularization schemes for minimum error entropy principle (Q5253870) (← links)
• Thresholded spectral algorithms for sparse approximations (Q5267950) (← links)
• Learning rates for regularized least squares ranking algorithm (Q5356934) (← links)
• Learning with Convex Loss and Indefinite Kernels (Q5378314) (← links)
• Support vector machines regression with unbounded sampling (Q5379431) (← links)
• Online Pairwise Learning Algorithms (Q5380417) (← links)
• Some properties of Gaussian reproducing kernel Hilbert spaces and their implications for function approximation and learning theory (Q5962345) (← links)
• Learning sparse and smooth functions by deep sigmoid nets (Q6109261) (← links)
• Error analysis of classification learning algorithms based on LUMs loss (Q6112861) (← links)
• Hybrid learning based on Fisher linear discriminant (Q6544564) (← links)
• Moduli of smoothness, \(K\)-functionals and Jackson-type inequalities associated with Kernel function approximation in learning theory (Q6587592) (← links)