The following pages link to LIBLINEAR (Q17033):
Displaying 50 items.
- Improving P300 speller performance by means of optimization and machine learning (Q2673824) (← links)
- On quantile regression in reproducing kernel Hilbert spaces with the data sparsity constraint (Q2810829) (← links)
- A unified view on multi-class support vector classification (Q2810837) (← links)
- Domain-adversarial training of neural networks (Q2810862) (← links)
- A multilevel framework for sparse optimization with application to inverse covariance estimation and logistic regression (Q2830631) (← links)
- Augmentable gamma belief networks (Q2834494) (← links)
- Language Processing with Perl and Prolog (Q2865938) (← links)
- Hybrid MPI/OpenMP parallel linear support vector machine training (Q2880955) (← links)
- Optimized cutting plane algorithm for large-scale risk minimization (Q2880969) (← links)
- Density ratio estimation in machine learning. Foreword by Thomas G. Dietterich (Q2881380) (← links)
- Bundle methods for regularized risk minimization (Q2896030) (← links)
- Training and testing low-degree polynomial data mappings via linear SVM (Q2896086) (← links)
- Tree decomposition for large-scale SVM problems (Q2896172) (← links)
- Orange: data mining toolbox in Python (Q2933897) (← links)
- (Q2933974) (← links)
- (Q2933977) (← links)
- (Q2933980) (← links)
- (Q2934109) (← links)
- Block Stochastic Gradient Iteration for Convex and Nonconvex Optimization (Q2945126) (← links)
- Feature selection combining linear support vector machines and concave optimization (Q3562388) (← links)
- (Q4558532) (← links)
- (Q4558572) (← links)
- Machine Learning for Text (Q4569273) (← links)
- Gap Safe screening rules for sparsity enforcing penalties (Q4637055) (← links)
- Optimization Methods for Large-Scale Machine Learning (Q4641709) (← links)
- Safe Feature Elimination in Sparse Supervised Learning (Q4906144) (← links)
- Skills in demand for ICT and statistical occupations: Evidence from web‐based job vacancies (Q4970416) (← links)
- A Derivative-Free Method for Structured Optimization Problems (Q4987276) (← links)
- A neighborhood prior constrained collaborative representation for classification (Q4990040) (← links)
- (Q4998861) (← links)
- Quadratic Convergence of Smoothing Newton's Method for 0/1 Loss Optimization (Q5020852) (← links)
- (Q5038378) (← links)
- ENIGMA Anonymous: Symbol-Independent Inference Guiding Machine (System Description) (Q5049022) (← links)
- A Subspace Acceleration Method for Minimization Involving a Group Sparsity-Inducing Regularizer (Q5072590) (← links)
- (Q5080613) (← links)
- Linear Algebra and Optimization for Machine Learning (Q5107275) (← links)
- Orthogonal canonical correlation analysis and applications (Q5135255) (← links)
- An efficient augmented Lagrangian method for support vector machine (Q5135259) (← links)
- (Q5149027) (← links)
- (Q5214210) (← links)
- The mRMR variable selection method: a comparative study for functional data (Q5222381) (← links)
- Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning (Q5254990) (← links)
- A Study on L2-Loss (Squared Hinge-Loss) Multiclass SVM (Q5378218) (← links)
- Large-Scale Linear RankSVM (Q5378345) (← links)
- Subsampled Hessian Newton Methods for Supervised Learning (Q5380307) (← links)
- (Q5381110) (← links)
- (Q5381111) (← links)
- Scikit-learn: machine learning in Python (Q5396710) (← links)
- Unsupervised learning of compositional sparse code for natural image representation (Q5420097) (← links)
- A generic coordinate descent solver for non-smooth convex optimisation (Q5865339) (← links)
