The following pages link to (Q3174103):
Displaying 16 items.
- Design of a multiple kernel learning algorithm for LS-SVM by convex programming (Q553288) (← links)
- Model selection for the LS-SVM. Application to handwriting recognition (Q733074) (← links)
- Efficient approximate leave-one-out cross-validation for kernel logistic regression (Q1009261) (← links)
- Learning kernel logistic regression in the presence of class label noise (Q1676947) (← links)
- Low rank updated LS-SVM classifiers for fast variable selection (Q1932006) (← links)
- Analysis of the IJCNN 2007 agnostic learning vs. prior knowledge challenge (Q1932020) (← links)
- Bayesian robust principal component analysis with adaptive singular value penalty (Q2193641) (← links)
- Granularity selection for cross-validation of SVM (Q2291784) (← links)
- Kernel learning at the first level of inference (Q2339392) (← links)
- Optimally regularised kernel Fisher discriminant classification (Q2383046) (← links)
- Overfitting in linear feature extraction for classification of high-dimensional image data (Q2416963) (← links)
- What is an optimal value of \(k\) in \(k\)-fold cross-validation in discrete Bayesian network analysis? (Q2667012) (← links)
- A subdivision-regularization framework for preventing over fitting of data by a model (Q2838213) (← links)
- The problem of over-fitting in the procedure of model selection based on structural risk minimization (Q2850403) (← links)
- On over-fitting in model selection and subsequent selection bias in performance evaluation (Q2896129) (← links)
- Neural network reconstruction of H'(z) and its application in teleparallel gravity (Q5878855) (← links)
