Pages that link to "Item:Q2782275"
From MaRDI portal
The following pages link to SVMTorch: Support vector machines for large-scale regression problems. (Q2782275):
Displaying 45 items.
- SVMTorch (Q24054) (← links)
- A nested heuristic for parameter tuning in support vector machines (Q336941) (← links)
- Supervised classification and mathematical optimization (Q339559) (← links)
- Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions (Q381435) (← links)
- An experience in using machine learning for short-term predictions in smart transportation systems (Q511935) (← links)
- Exploiting separability in large-scale linear support vector machine training (Q540649) (← links)
- Artificial neural networks applied to cancer detection in a breast screening programme (Q622959) (← links)
- Parallel algorithm for training multiclass proximal support vector machines (Q628877) (← links)
- Linear programming approaches for multicategory support vector machines (Q706935) (← links)
- Regularized multiple criteria linear programs for classification (Q848372) (← links)
- An improved robust and sparse twin support vector regression via linear programming (Q894317) (← links)
- Iterative sliced inverse regression for segmentation of ultrasound and MR images (Q996432) (← links)
- Recursive reduced least squares support vector regression (Q1010090) (← links)
- Parameter detection of thin films from their X-ray reflectivity by support vector machines. (Q1427212) (← links)
- Solving the slate tile classification problem using a DAGSVM multiclassification algorithm based on SVM binary classifiers with a one-versus-all approach (Q1644084) (← links)
- Predicting specific surface areas of layered double hydroxides based on support vector regression integrated with a residual bootstrapping method (Q1649174) (← links)
- An improved way to make large-scale SVR learning practical (Q1773787) (← links)
- TSVR: an efficient twin support vector machine for regression (Q1784563) (← links)
- A critical appraisal of design of experiments for uncertainty quantification (Q1787391) (← links)
- Robust support vector regression in the primal (Q1932127) (← links)
- Cutting-plane training of structural SVMs (Q1959520) (← links)
- Unified regression model in fitting potential energy surfaces for quantum dynamics (Q2084807) (← links)
- Clusterwise support vector linear regression (Q2189912) (← links)
- Recursive projection twin support vector machine via within-class variance minimization (Q2276005) (← links)
- Control-based algorithms for high dimensional online learning (Q2297419) (← links)
- A difference of convex optimization algorithm for piecewise linear regression (Q2313774) (← links)
- Overlapping radial basis function interpolants for spectrally accurate approximation of functions of eigenvalues with application to buckling of composite plates (Q2364220) (← links)
- An adaptive error penalization method for training an efficient and generalized SVM (Q2369549) (← links)
- Non-parametric classifier-independent feature selection (Q2369609) (← links)
- Multicriteria analysis tools in real-life problems (Q2426003) (← links)
- An improved gradient projection-based decomposition technique for support vector machines (Q2468369) (← links)
- Non-parametric regression methods (Q2468374) (← links)
- FS\(_-\)SFS: a novel feature selection method for support vector machines (Q2495927) (← links)
- An efficient support vector machine learning method with second-order cone programming for large-scale problems (Q2576752) (← links)
- Machine learning approach to color constancy (Q2643773) (← links)
- Binary separation and training support vector machines (Q2890532) (← links)
- A parallel decomposition algorithm for training multiclass kernel-based vector machines (Q3093054) (← links)
- Short term forecasting with support vector machines and application to stock price prediction (Q3549307) (← links)
- Robust Optimizers for Nonlinear Programming in Approximate Dynamic Programming (Q3564534) (← links)
- A Note on the Decomposition Methods for Support Vector Regression (Q4542417) (← links)
- A Parallel Mixture of SVMs for Very Large Scale Problems (Q4542440) (← links)
- (Q4999025) (← links)
- Gradient projection methods for quadratic programs and applications in training support vector machines (Q5317754) (← links)
- On the working set selection in gradient projection-based decomposition techniques for support vector machines (Q5717544) (← links)
- Class imbalance learning using fuzzy ART and intuitionistic fuzzy twin support vector machines (Q6089967) (← links)
