Pages that link to "Item:Q3556804"
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The following pages link to Regularization Techniques and Suboptimal Solutions to Optimization Problems in Learning from Data (Q3556804):
Displaying 18 items.
- Some approaches to the solution of optimization problems in supervised learning (Q267571) (← links)
- Can dictionary-based computational models outperform the best linear ones? (Q456017) (← links)
- Functional optimization by variable-basis approximation schemes (Q538282) (← links)
- Optimization problems in statistical learning: duality and optimality conditions (Q545113) (← links)
- On spectral windows in supervised learning from data (Q1675817) (← links)
- An algorithm for curve identification in the presence of curve intersections (Q1721348) (← links)
- Laplacian twin support vector machine for semi-supervised classification (Q1942706) (← links)
- From inexact optimization to learning via gradient concentration (Q2111477) (← links)
- Alternating step size method for solving ill-posed linear operator equations in energetic space (Q2161077) (← links)
- The weight-decay technique in learning from data: an optimization point of view (Q2271792) (← links)
- Distributed semi-supervised support vector machines (Q2418194) (← links)
- Iterative regularization for learning with convex loss functions (Q2810891) (← links)
- A subdivision-regularization framework for preventing over fitting of data by a model (Q2838213) (← links)
- Regularizing algorithms with optimal and extra-optimal quality (Q2963863) (← links)
- Flexible constraints for regularization in learning from data (Q3156899) (← links)
- Learning with Boundary Conditions (Q5327200) (← links)
- Levenberg-Marquardt multi-classification using hinge loss function (Q6055118) (← links)
- Sign stochastic gradient descents without bounded gradient assumption for the finite sum minimization (Q6072513) (← links)
