The following pages link to (Q4784077):
Displaying 20 items.
- Impulse response constrained LS-SVM modelling for MIMO Hammerstein system identification (Q5382603) (← links)
- Adaptive control scheme based on the least squares support vector machine network (Q5403424) (← links)
- System identification techniques based on support vector machines without bias term (Q5408084) (← links)
- Semiparametric Regression of Multidimensional Genetic Pathway Data: Least‐Squares Kernel Machines and Linear Mixed Models (Q5449906) (← links)
- Modelling Beyond Regression Functions: An Application of Multimodal Regression to Speed–Flow Data (Q5757848) (← links)
- Determinantal Point Processes Implicitly Regularize Semiparametric Regression Problems (Q5868548) (← links)
- Multicategory proximal support vector machine classifiers (Q5916203) (← links)
- Multicategory proximal support vector machine classifiers (Q5921686) (← links)
- Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints (Q6079088) (← links)
- On Lagrangian L2-norm pinball twin bounded support vector machine via unconstrained convex minimization (Q6092065) (← links)
- Unified SVM algorithm based on LS-DC loss (Q6134358) (← links)
- Cuckoo search-based least squares support vector machine models for optimum tuning of tuned mass dampers (Q6492989) (← links)
- Sparse \(L_0\)-norm least squares support vector machine with feature selection (Q6544594) (← links)
- A refinement of the stability test for reproducing kernel Hilbert spaces (Q6546871) (← links)
- Homocentric quadratic surfaces and maximum margin approach for imbalanced data classification (Q6550076) (← links)
- Machine learning techniques in nested stochastic simulations for life insurance (Q6579522) (← links)
- Mercer kernel absolute integrability is only sufficient for RKHS stability (Q6590441) (← links)
- Basics of SVM method and least squares SVM (Q6606423) (← links)
- Solving ordinary differential equations by LS-SVM (Q6606432) (← links)
- A new method based on least-squares support vector regression for solving optimal control problems. (Q6648149) (← links)
