The following pages link to Learning Theory (Q4680890):
Displaying 15 items.
- Kernel methods in system identification, machine learning and function estimation: a survey (Q462325) (← links)
- A coordinate gradient descent method for linearly constrained smooth optimization and support vector machines training (Q711381) (← links)
- Basis of the algorithm of asymptotic decomposition for a finite number of approximations (Q1112220) (← links)
- Decomposition methods: A new proof of convergence (Q1324723) (← links)
- Polynomial-time decomposition algorithms for support vector machines (Q1397416) (← links)
- New ideas for proving convergence of decomposition methods (Q1894943) (← links)
- On the complexity of working set selection (Q2381582) (← links)
- A Note on the Decomposition Methods for Support Vector Regression (Q4542417) (← links)
- On the convergence of a modified version of SVM<i><sup>light</sup></i>algorithm (Q5317752) (← links)
- On the Effective Region of Convergence of the Decomposition Series Solution (Q5396355) (← links)
- Algorithmic Learning Theory (Q5464506) (← links)
- Algorithmic Learning Theory (Q5464507) (← links)
- Learning Theory (Q5473619) (← links)
- A simple decomposition method for support vector machines (Q5959964) (← links)
- A direct proof of Conley's decomposition for well-posed hybrid inC30clusions (Q6084724) (← links)
