Covering number bounds of certain regularized linear function classes
The Journal of Machine Learning Research
Statistical Analysis of Some Multi-Category Large Margin Classification Methods
The Journal of Machine Learning Research
The Entire Regularization Path for the Support Vector Machine
The Journal of Machine Learning Research
Nonparametric Quantile Estimation
The Journal of Machine Learning Research
Considering Cost Asymmetry in Learning Classifiers
The Journal of Machine Learning Research
Using neural networks to model conditional multivariate densities
Neural Computation
Density Ratio Estimation: A New Versatile Tool for Machine Learning
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Bi-Level Path Following for Cross Validated Solution of Kernel Quantile Regression
The Journal of Machine Learning Research
Multiple incremental decremental learning of support vector machines
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
The goal of regression analysis is to describe the stochastic relationship between an input vector x and a scalar output y. This can be achieved by estimating the entire conditional density p(y ∣ x). In this letter, we present a new approach for nonparametric conditional density estimation. We develop a piecewise-linear path-following method for kernel-based quantile regression. It enables us to estimate the cumulative distribution function of p(y ∣ x) in piecewise-linear form for all x in the input domain. Theoretical analyses and experimental results are presented to show the effectiveness of the approach.