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Yiming Ying
Researcher at University at Albany, SUNY
Publications - 107
Citations - 4132
Yiming Ying is an academic researcher from University at Albany, SUNY. The author has contributed to research in topics: Generalization & Maximization. The author has an hindex of 30, co-authored 101 publications receiving 3508 citations. Previous affiliations of Yiming Ying include University of Bristol & State University of New York System.
Papers
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Journal Article
Distance metric learning with eigenvalue optimization
Yiming Ying,Peng Li +1 more
TL;DR: A novel metric learning approach called DML-eig is introduced which is shown to be equivalent to a well-known eigen value optimization problem called minimizing the maximal eigenvalue of a symmetric matrix.
Proceedings Article
A Spectral Regularization Framework for Multi-Task Structure Learning
TL;DR: A framework for solving this problem, which is based on regularization with spectral functions of matrices, and indicates that the algorithm scales well with the number of tasks and improves on state of the art statistical performance.
Journal ArticleDOI
Learning Rates of Least-Square Regularized Regression
TL;DR: A novel regularization approach is presented, which yields satisfactory learning rates that depend on the approximation property and on the capacity of the reproducing kernel Hilbert space measured by covering numbers.
Book
Learning with Support Vector Machines
Colin Campbell,Yiming Ying +1 more
TL;DR: This book starts with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise, and shows that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees.
Journal Article
Support Vector Machine Soft Margin Classifiers: Error Analysis
TL;DR: A projection operator is introduced, which leads to better sample error estimates especially for small complexity kernels, and the choice of the regularization parameter plays an important role in the analysis.