2008 |
36 | EE | Trevor Hastie,
Jerome Friedman,
Robert Tibshirani:
Regularization paths and coordinate descent.
KDD 2008: 3 |
35 | EE | Ping Li,
Kenneth Ward Church,
Trevor Hastie:
One sketch for all: Theory and Application of Conditional Random Sampling.
NIPS 2008: 953-960 |
2007 |
34 | EE | Ping Li,
Trevor Hastie,
Kenneth Ward Church:
Nonlinear Estimators and Tail Bounds for Dimension Reduction in l 1 Using Cauchy Random Projections.
COLT 2007: 514-529 |
33 | EE | Ping Li,
Trevor Hastie:
A Unified Near-Optimal Estimator For Dimension Reduction in lalpha(0 < alpha <= 2) Using Stable Random Projections.
NIPS 2007 |
2006 |
32 | EE | Ping Li,
Trevor Hastie,
Kenneth Ward Church:
Improving Random Projections Using Marginal Information.
COLT 2006: 635-649 |
31 | EE | Ping Li,
Trevor Hastie,
Kenneth Ward Church:
Very sparse random projections.
KDD 2006: 287-296 |
30 | EE | Ping Li,
Kenneth Ward Church,
Trevor Hastie:
Conditional Random Sampling: A Sketch-based Sampling Technique for Sparse Data.
NIPS 2006: 873-880 |
29 | EE | Ping Li,
Trevor Hastie,
Kenneth Ward Church:
Nonlinear Estimators and Tail Bounds for Dimension Reduction in $l_1$ Using Cauchy Random Projections
CoRR abs/cs/0610155: (2006) |
2005 |
28 | EE | Dirk Ormoneit,
Michael J. Black,
Trevor Hastie,
Hedvig Kjellström:
Representing cyclic human motion using functional analysis.
Image Vision Comput. 23(14): 1264-1276 (2005) |
2004 |
27 | EE | Philip Beineke,
Trevor Hastie,
Shivakumar Vaithyanathan:
The Sentimental Factor: Improving Review Classification Via Human-Provided Information.
ACL 2004: 263-270 |
26 | EE | Saharon Rosset,
Ji Zhu,
Hui Zou,
Trevor Hastie:
A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning.
NIPS 2004 |
25 | EE | Trevor Hastie,
Saharon Rosset,
Robert Tibshirani,
Ji Zhu:
The Entire Regularization Path for the Support Vector Machine.
NIPS 2004 |
24 | EE | Robert Tibshirani,
Trevor Hastie,
Balasubramanian Narasimhan,
Scott Soltys,
Gongyi Shi,
Albert Koong,
Quynh-Thu Le:
Sample classification from protein mass spectrometry, by 'peak probability contrasts'.
Bioinformatics 20(17): 3034-3044 (2004) |
23 | 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) |
22 | 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 |
21 | EE | Saharon Rosset,
Ji Zhu,
Trevor Hastie:
Boosting and support vector machines as optimal separators.
DRR 2003: 1-7 |
20 | EE | Ji Zhu,
Saharon Rosset,
Trevor Hastie,
Robert Tibshirani:
1-norm Support Vector Machines.
NIPS 2003 |
19 | EE | Saharon Rosset,
Ji Zhu,
Trevor Hastie:
Margin Maximizing Loss Functions.
NIPS 2003 |
18 | EE | Trevor Hastie,
Robert Tibshirani,
Jerome Friedman:
Note on "Comparison of Model Selection for Regression" by Vladimir Cherkassky and Yunqian Ma.
Neural Computation 15(7): 1477-1480 (2003) |
2002 |
17 | EE | Ji Zhu,
Trevor Hastie:
Support Vector Machines, Kernel Logistic Regression and Boosting.
Multiple Classifier Systems 2002: 16-26 |
16 | EE | Trevor Hastie,
Robert Tibshirani:
Independent Components Analysis through Product Density Estimation.
NIPS 2002: 649-656 |
2001 |
15 | EE | Ji Zhu,
Trevor Hastie:
Kernel Logistic Regression and the Import Vector Machine.
NIPS 2001: 1081-1088 |
14 | | Olga G. Troyanskaya,
Michael Cantor,
Gavin Sherlock,
Patrick O. Brown,
Trevor Hastie,
Robert Tibshirani,
David Botstein,
Russ B. Altman:
Missing value estimation methods for DNA microarrays.
Bioinformatics 17(6): 520-525 (2001) |
2000 |
13 | | Dirk Ormoneit,
Hedvig Sidenbladh,
Michael J. Black,
Trevor Hastie:
Learning and Tracking Cyclic Human Motion.
NIPS 2000: 894-900 |
1999 |
12 | EE | Dirk Ormoneit,
Trevor Hastie:
Optimal Kernel Shapes for Local Linear Regression.
NIPS 1999: 540-546 |
1998 |
11 | EE | Thomas D. Wu,
Trevor Hastie,
Scott C. Schmidler,
Douglas L. Brutlag:
Regression analysis of multiple protein structures.
RECOMB 1998: 276-284 |
10 | | Thomas D. Wu,
Scott C. Schmidler,
Trevor Hastie,
Douglas L. Brutlag:
Regression Analysis of Multiple Protein Structures.
Journal of Computational Biology 5(3): 585-596 (1998) |
1997 |
9 | | Y. Dan Rubinstein,
Trevor Hastie:
Discriminative vs Informative Learning.
KDD 1997: 49-53 |
8 | | Trevor Hastie,
Robert Tibshirani:
Classification by Pairwise Coupling.
NIPS 1997 |
7 | | Gareth James,
Trevor Hastie:
The Error Coding and Substitution PaCTs.
NIPS 1997 |
1996 |
6 | EE | Trevor Hastie,
Robert Tibshirani:
Discriminant Adaptive Nearest Neighbor Classification.
IEEE Trans. Pattern Anal. Mach. Intell. 18(6): 607-616 (1996) |
1995 |
5 | | Trevor Hastie,
Robert Tibshirani:
Discriminant Adaptive Nearest Neighbor Classification.
KDD 1995: 142-149 |
4 | EE | Trevor Hastie,
Robert Tibshirani:
Discriminant Adaptive Nearest Neighbor Classification and Regression.
NIPS 1995: 409-415 |
1994 |
3 | EE | Trevor Hastie,
Patrice Simard:
Learning Prototype Models for Tangent Distance.
NIPS 1994: 999-1006 |
2 | | Winston Nelson,
William Turin,
Trevor Hastie:
Statistical Methods for On-Line Signature Verification.
IJPRAI 8(3): 749-770 (1994) |
1990 |
1 | | Eyal Kishon,
Trevor Hastie,
Haim J. Wolfson:
3-D Curve Matching Using Splines.
ECCV 1990: 589-591 |