Pages that link to "Item:Q4400270"

The following pages link to The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network (Q4400270):

Displaying 50 items.

• Tikhonov, Ivanov and Morozov regularization for support vector machine learning (Q285946) (← links)
• The learning rate of \(l_2\)-coefficient regularized classification with strong loss (Q383667) (← links)
• Approximation by multivariate Bernstein-Durrmeyer operators and learning rates of least-squares regularized regression with multivariate polynomial kernels (Q390534) (← links)
• Sequence classification via large margin hidden Markov models (Q408657) (← links)
• Robust cutpoints in the logical analysis of numerical data (Q412323) (← links)
• Robustness and generalization (Q420915) (← links)
• Minimizing loss probability bounds for portfolio selection (Q439383) (← links)
• Classification with polynomial kernels and \(l^1\)-coefficient regularization (Q514786) (← links)
• Optimal convergence rate of the universal estimation error (Q516004) (← links)
• Logistic classification with varying gaussians (Q534984) (← links)
• Learning rates for multi-kernel linear programming classifiers (Q537615) (← links)
• On the role of norm constraints in portfolio selection (Q645500) (← links)
• Unified approach to coefficient-based regularized regression (Q651513) (← links)
• Generalization performance of least-square regularized regression algorithm with Markov chain samples (Q662073) (← links)
• Analysis of a multi-category classifier (Q714014) (← links)
• Multi-category classifiers and sample width (Q736602) (← links)
• Terminated Ramp--Support Vector machines: A nonparametric data dependent kernel (Q858897) (← links)
• On the generalization error of fixed combinations of classifiers (Q881592) (← links)
• Estimation of the misclassification error for multicategory support vector machine classification (Q943493) (← links)
• Learning rates for regularized classifiers using multivariate polynomial kernels (Q958248) (← links)
• Large margin cost-sensitive learning of conditional random fields (Q992002) (← links)
• Analysis of support vector machines regression (Q1022433) (← links)
• Maximal width learning of binary functions (Q1041230) (← links)
• The complexity of model classes, and smoothing noisy data (Q1274410) (← links)
• A re-weighting strategy for improving margins (Q1605285) (← links)
• One-class classification with extreme learning machine (Q1665625) (← links)
• A simpler approach to coefficient regularized support vector machines regression (Q1722337) (← links)
• A probabilistic learning algorithm for robust modeling using neural networks with random weights (Q1749195) (← links)
• Efficient extreme learning machine via very sparse random projection (Q1797950) (← links)
• Boosting the margin: a new explanation for the effectiveness of voting methods (Q1807156) (← links)
• Overparameterised adaptive controllers can reduce non-singular costs. (Q1853446) (← links)
• Bounding the generalization error of convex combinations of classifiers: Balancing the dimensionality and the margins. (Q1872344) (← links)
• Generalization error of combined classifiers. (Q1872713) (← links)
• Relation between weight size and degree of over-fitting in neural network regression (Q1931976) (← links)
• A tight upper bound on the generalization error of feedforward neural networks (Q1982395) (← links)
• Optimal control of complex systems based on improved dual heuristic dynamic programming algorithm (Q1992863) (← links)
• Analysis of a two-layer neural network via displacement convexity (Q1996787) (← links)
• Robust extreme learning machine for modeling with unknown noise (Q2005404) (← links)
• A selective overview of deep learning (Q2038303) (← links)
• Regularisation of neural networks by enforcing Lipschitz continuity (Q2051250) (← links)
• Nonparametric regression with modified ReLU networks (Q2081757) (← links)
• Measurement error models: from nonparametric methods to deep neural networks (Q2092892) (← links)
• Extreme learning machine for a new hybrid morphological/linear perceptron (Q2185695) (← links)
• Quantitative convergence analysis of kernel based large-margin unified machines (Q2191836) (← links)
• The weight-decay technique in learning from data: an optimization point of view (Q2271792) (← links)
• Kernel learning at the first level of inference (Q2339392) (← links)
• Estimates of covering numbers of convex sets with slowly decaying orthogonal subsets (Q2381815) (← links)
• \(L_{p}\)-norm Sauer-Shelah lemma for margin multi-category classifiers (Q2402375) (← links)
• Two fast and accurate heuristic RBF learning rules for data classification (Q2418116) (← links)
• Statistical performance of support vector machines (Q2426613) (← links)