Pages that link to "Item:Q870339"
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The following pages link to On regularization algorithms in learning theory (Q870339):
Displaying 50 items.
- Tikhonov, Ivanov and Morozov regularization for support vector machine learning (Q285946) (← links)
- Multi-penalty regularization in learning theory (Q306697) (← links)
- New horizons in statistical decision theory. Abstracts from the workshop held September 7--13, 2014. (Q347222) (← links)
- Regularized least square regression with unbounded and dependent sampling (Q369717) (← links)
- Sharp learning rates of coefficient-based \(l^q\)-regularized regression with indefinite kernels (Q370936) (← links)
- Consistency analysis of spectral regularization algorithms (Q437553) (← links)
- Multi-output learning via spectral filtering (Q439000) (← links)
- Multi-parameter regularization and its numerical realization (Q537875) (← links)
- Least square regression with indefinite kernels and coefficient regularization (Q617706) (← links)
- Convergence rate of kernel canonical correlation analysis (Q659987) (← links)
- Optimal rates for regularization of statistical inverse learning problems (Q667648) (← links)
- Learning rates for kernel-based expectile regression (Q669274) (← links)
- A neural network algorithm to pattern recognition in inverse problems (Q905327) (← links)
- Elastic-net regularization in learning theory (Q1023403) (← links)
- Thresholding projection estimators in functional linear models (Q1049544) (← links)
- On principal components regression, random projections, and column subsampling (Q1616329) (← links)
- Optimal learning rates for kernel partial least squares (Q1645280) (← links)
- A linear functional strategy for regularized ranking (Q1669294) (← links)
- On spectral windows in supervised learning from data (Q1675817) (← links)
- Convergence analysis of an empirical eigenfunction-based ranking algorithm with truncated sparsity (Q1722329) (← links)
- Distributed kernel-based gradient descent algorithms (Q1745365) (← links)
- A meta-learning approach to the regularized learning -- case study: blood glucose prediction (Q1941597) (← links)
- A general learning framework using local and global regularization (Q1957872) (← links)
- Adaptive kernel methods using the balancing principle (Q1959089) (← links)
- The \(\mathrm{r}\)-\(\mathrm{d}\) class predictions in linear mixed models (Q2048216) (← links)
- A statistical learning assessment of Huber regression (Q2054280) (← links)
- On a regularization of unsupervised domain adaptation in RKHS (Q2075006) (← links)
- From inexact optimization to learning via gradient concentration (Q2111477) (← links)
- Machine learning with kernels for portfolio valuation and risk management (Q2120539) (← links)
- Online gradient descent algorithms for functional data learning (Q2121498) (← links)
- Fast rates of minimum error entropy with heavy-tailed noise (Q2168008) (← links)
- Manifold regularization based on Nyström type subsampling (Q2175018) (← links)
- Learning rates for the kernel regularized regression with a differentiable strongly convex loss (Q2191832) (← links)
- Convergence analysis of Tikhonov regularization for non-linear statistical inverse problems (Q2192321) (← links)
- Kernel gradient descent algorithm for information theoretic learning (Q2223567) (← links)
- Learning sets with separating kernels (Q2252512) (← links)
- On empirical eigenfunction-based ranking with \(\ell^1\) norm regularization (Q2256621) (← links)
- Balancing principle in supervised learning for a general regularization scheme (Q2278452) (← links)
- Optimal learning rates for distribution regression (Q2283125) (← links)
- Optimal rates for spectral algorithms with least-squares regression over Hilbert spaces (Q2300763) (← links)
- Concentration of weakly dependent Banach-valued sums and applications to statistical learning methods (Q2325378) (← links)
- Optimal rates for coefficient-based regularized regression (Q2330932) (← links)
- Analysis of singular value thresholding algorithm for matrix completion (Q2338558) (← links)
- The convergence rates of Shannon sampling learning algorithms (Q2392948) (← links)
- Multi-task learning via linear functional strategy (Q2407408) (← links)
- Distributed learning with multi-penalty regularization (Q2415399) (← links)
- Iterative regularization for learning with convex loss functions (Q2810891) (← links)
- Half supervised coefficient regularization for regression learning with unbounded sampling (Q2855757) (← links)
- Coefficient regularized regression with non-iid sampling (Q2885538) (← links)
- Regularizing algorithms with optimal and extra-optimal quality (Q2963863) (← links)
