Large margin unified machines with non-i.i.d. process

Cites Work

Title not available (Why is that?)
Title not available (Why is that?)
Title not available (Why is that?)
Distance-Weighted Discrimination
Logistic classification with varying gaussians
Estimating conditional quantiles with the help of the pinball loss
Classification with non-i.i.d. sampling
Regularized least square regression with dependent samples
Multi-kernel regularized classifiers
Fast rates for support vector machines using Gaussian kernels
Rates of convergence for empirical processes of stationary mixing sequences
The covering number in learning theory
Support-vector networks
Conditional quantiles with varying Gaussians
Modeling interactive components by coordinate kernel polynomial models
Quantitative convergence analysis of kernel based large-margin unified machines
Lasso guarantees for \(\beta \)-mixing heavy-tailed time series
Learning rates of regularized regression for exponentially strongly mixing sequence
Learning rates of least-square regularized regression
Classification with Gaussians and convex loss
Learning Theory
Capacity of reproducing kernel spaces in learning theory
ONLINE LEARNING WITH MARKOV SAMPLING
The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network
Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators
A Statistical Learning Approach to Modal Regression
Comparison theorems on large-margin learning
Hard or Soft Classification? Large-Margin Unified Machines
The elements of statistical learning. Data mining, inference, and prediction

This page was built for publication: Large margin unified machines with non-i.i.d. process

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6599669)