2008 |
26 | EE | Li Wang,
Ji Zhu,
Hui Zou:
Hybrid huberized support vector machines for microarray classification and gene selection.
Bioinformatics 24(3): 412-419 (2008) |
2007 |
25 | EE | Lacey Gunter,
Ji Zhu,
Susan Murphy:
Variable Selection for Optimal Decision Making.
AIME 2007: 149-154 |
24 | EE | Saharon Rosset,
Grzegorz Swirszcz,
Nathan Srebro,
Ji Zhu:
l1 Regularization in Infinite Dimensional Feature Spaces.
COLT 2007: 544-558 |
23 | EE | Li Wang,
Ji Zhu,
Hui Zou:
Hybrid huberized support vector machines for microarray classification.
ICML 2007: 983-990 |
22 | EE | Ji Zhu,
Hui Zou:
Variable Selection for the Linear Support Vector Machine.
Trends in Neural Computation 2007: 35-59 |
21 | EE | Youjuan Li,
Ji Zhu:
Analysis of array CGH data for cancer studies using fused quantile regression.
Bioinformatics 23(18): 2470-2476 (2007) |
20 | EE | Sijian Wang,
Ji Zhu:
Improved centroids estimation for the nearest shrunken centroid classifier.
Bioinformatics 23(8): 972-979 (2007) |
19 | EE | Lacey Gunter,
Ji Zhu:
Efficient Computation and Model Selection for the Support Vector Regression.
Neural Computation 19(6): 1633-1655 (2007) |
2006 |
18 | EE | David Abramson,
Amanda Lynch,
Hiroshi Takemiya,
Yusuke Tanimura,
Susumu Date,
Haruki Nakamura,
Karpjoo Jeong,
Suntae Hwang,
Ji Zhu,
Zhonghua Lu,
Céline Amoreira,
Kim Baldridge,
Hurng-Chun Lee,
Chi-Wei Wang,
Horng-Liang Shih,
Tomas E. Molina,
Wilfred W. Li,
Peter W. Arzberger:
Deploying Scientific Applications to the PRAGMA Grid Testbed: Strategies and Lessons.
CCGRID 2006: 241-248 |
17 | EE | Ji Zhu,
Ang Guo,
Zhonghua Lu,
Yongwei Wu,
Bin Shen,
Xuebin Chi:
Analysis of the Bioinformatics Grid Technique Applications in China.
CCGRID 2006: 44 |
16 | EE | Li Wang,
Michael D. Gordon,
Ji Zhu:
Regularized Least Absolute Deviations Regression and an Efficient Algorithm for Parameter Tuning.
ICDM 2006: 690-700 |
15 | EE | Zhili Wu,
Chun-hung Li,
Ji Zhu,
Jian Huang:
A Semi-supervised SVM for Manifold Learning.
ICPR (2) 2006: 490-493 |
2005 |
14 | EE | Lacey Gunter,
Ji Zhu:
Computing the Solution Path for the Regularized Support Vector Regression.
NIPS 2005 |
2004 |
13 | EE | Saharon Rosset,
Ji Zhu,
Hui Zou,
Trevor Hastie:
A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning.
NIPS 2004 |
12 | EE | Trevor Hastie,
Saharon Rosset,
Robert Tibshirani,
Ji Zhu:
The Entire Regularization Path for the Support Vector Machine.
NIPS 2004 |
11 | EE | James Mauro,
Ji Zhu,
Ira Pramanick:
The System Recovery Benchmark.
PRDC 2004: 271-280 |
10 | EE | Trevor Hastie,
Saharon Rosset,
Robert Tibshirani,
Ji Zhu:
The Entire Regularization Path for the Support Vector Machine.
Journal of Machine Learning Research 5: 1391-1415 (2004) |
9 | EE | Saharon Rosset,
Ji Zhu,
Trevor Hastie:
Boosting as a Regularized Path to a Maximum Margin Classifier.
Journal of Machine Learning Research 5: 941-973 (2004) |
2003 |
8 | EE | Ira Pramanick,
James Mauro,
Ji Zhu:
A System Recovery Benchmark for Clusters.
CLUSTER 2003: 387-394 |
7 | EE | Saharon Rosset,
Ji Zhu,
Trevor Hastie:
Boosting and support vector machines as optimal separators.
DRR 2003: 1-7 |
6 | EE | Ji Zhu,
James Mauro,
Ira Pramanick:
Robustness Benchmarking for Hardware Maintenance Events.
DSN 2003: 115-122 |
5 | EE | Ji Zhu,
Saharon Rosset,
Trevor Hastie,
Robert Tibshirani:
1-norm Support Vector Machines.
NIPS 2003 |
4 | EE | Saharon Rosset,
Ji Zhu,
Trevor Hastie:
Margin Maximizing Loss Functions.
NIPS 2003 |
2002 |
3 | EE | Dong Tang,
Ji Zhu,
Roy Andrada:
Automatic Generation of Availability Models in RAScad.
DSN 2002: 488-494 |
2 | EE | Ji Zhu,
Trevor Hastie:
Support Vector Machines, Kernel Logistic Regression and Boosting.
Multiple Classifier Systems 2002: 16-26 |
2001 |
1 | EE | Ji Zhu,
Trevor Hastie:
Kernel Logistic Regression and the Import Vector Machine.
NIPS 2001: 1081-1088 |