Pages that link to "Item:Q5959964"
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The following pages link to A simple decomposition method for support vector machines (Q5959964):
Displaying 28 items.
- Two new decomposition algorithms for training bound-constrained support vector machines (Q275574) (← links)
- Support vector machines maximizing geometric margins for multi-class classification (Q287612) (← links)
- Cross-term elimination in Wigner distribution based on 2D signal processing techniques (Q612652) (← links)
- A convergent decomposition method for box-constrained optimization problems (Q839804) (← links)
- A simple decomposition algorithm for support vector machines with polynomial-time convergence (Q885211) (← links)
- SVM classification: Its contents and challenges (Q1430966) (← links)
- A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach (Q1761095) (← links)
- Distance metric learning for graph structured data (Q2071320) (← links)
- A unified convergence framework for nonmonotone inexact decomposition methods (Q2301131) (← links)
- On the complexity of working set selection (Q2381582) (← links)
- An improved gradient projection-based decomposition technique for support vector machines (Q2468369) (← links)
- Efficient optimization of support vector machine learning parameters for unbalanced datasets (Q2503014) (← links)
- An efficient support vector machine learning method with second-order cone programming for large-scale problems (Q2576752) (← links)
- Training <i>v</i>-Support Vector Regression: Theory and Algorithms (Q3149528) (← links)
- A Note on the Decomposition Methods for Support Vector Regression (Q4542417) (← links)
- (Q4637010) (← links)
- Learning Theory (Q4680890) (← links)
- Adaptive Two-Pass Median Filter Based on Support Vector Machines for Image Restoration (Q4819828) (← links)
- Decomposition Methods for Linear Support Vector Machines (Q4832489) (← links)
- (Q4998894) (← links)
- Parameter selection of Gaussian kernel SVM based on local density of training set (Q5153272) (← links)
- Gradient projection methods for quadratic programs and applications in training support vector machines (Q5317754) (← links)
- Algorithmic Learning Theory (Q5464506) (← links)
- Algorithmic Learning Theory (Q5464507) (← links)
- On the working set selection in gradient projection-based decomposition techniques for support vector machines (Q5717544) (← links)
- Nonlinear optimization and support vector machines (Q5915690) (← links)
- Nonlinear optimization and support vector machines (Q5918754) (← links)
- Heterogeneous sensor data fusion for target classification using adaptive distance function (Q6602044) (← links)
