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Trevor Hastie

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2008
36EETrevor Hastie, Jerome Friedman, Robert Tibshirani: Regularization paths and coordinate descent. KDD 2008: 3
35EEPing Li, Kenneth Ward Church, Trevor Hastie: One sketch for all: Theory and Application of Conditional Random Sampling. NIPS 2008: 953-960
2007
34EEPing 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
33EEPing Li, Trevor Hastie: A Unified Near-Optimal Estimator For Dimension Reduction in lalpha(0 < alpha <= 2) Using Stable Random Projections. NIPS 2007
2006
32EEPing Li, Trevor Hastie, Kenneth Ward Church: Improving Random Projections Using Marginal Information. COLT 2006: 635-649
31EEPing Li, Trevor Hastie, Kenneth Ward Church: Very sparse random projections. KDD 2006: 287-296
30EEPing Li, Kenneth Ward Church, Trevor Hastie: Conditional Random Sampling: A Sketch-based Sampling Technique for Sparse Data. NIPS 2006: 873-880
29EEPing 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
28EEDirk Ormoneit, Michael J. Black, Trevor Hastie, Hedvig Kjellström: Representing cyclic human motion using functional analysis. Image Vision Comput. 23(14): 1264-1276 (2005)
2004
27EEPhilip Beineke, Trevor Hastie, Shivakumar Vaithyanathan: The Sentimental Factor: Improving Review Classification Via Human-Provided Information. ACL 2004: 263-270
26EESaharon Rosset, Ji Zhu, Hui Zou, Trevor Hastie: A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning. NIPS 2004
25EETrevor Hastie, Saharon Rosset, Robert Tibshirani, Ji Zhu: The Entire Regularization Path for the Support Vector Machine. NIPS 2004
24EERobert 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)
23EETrevor 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)
22EESaharon 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
21EESaharon Rosset, Ji Zhu, Trevor Hastie: Boosting and support vector machines as optimal separators. DRR 2003: 1-7
20EEJi Zhu, Saharon Rosset, Trevor Hastie, Robert Tibshirani: 1-norm Support Vector Machines. NIPS 2003
19EESaharon Rosset, Ji Zhu, Trevor Hastie: Margin Maximizing Loss Functions. NIPS 2003
18EETrevor 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
17EEJi Zhu, Trevor Hastie: Support Vector Machines, Kernel Logistic Regression and Boosting. Multiple Classifier Systems 2002: 16-26
16EETrevor Hastie, Robert Tibshirani: Independent Components Analysis through Product Density Estimation. NIPS 2002: 649-656
2001
15EEJi 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
12EEDirk Ormoneit, Trevor Hastie: Optimal Kernel Shapes for Local Linear Regression. NIPS 1999: 540-546
1998
11EEThomas 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
6EETrevor 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
4EETrevor Hastie, Robert Tibshirani: Discriminant Adaptive Nearest Neighbor Classification and Regression. NIPS 1995: 409-415
1994
3EETrevor 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

Coauthor Index

1Russ B. Altman [14]
2Philip Beineke [27]
3Michael J. Black [13] [28]
4David Botstein [14]
5Patrick O. Brown [14]
6Douglas L. Brutlag [10] [11]
7Michael Cantor [14]
8Kenneth Ward Church [29] [30] [31] [32] [34] [35]
9Jerome Friedman [18] [36]
10Gareth James [7]
11Eyal Kishon [1]
12Hedvig Kjellström (Hedvig Sidenbladh) [13] [28]
13Albert Koong [24]
14Quynh-Thu Le [24]
15Ping Li [29] [30] [31] [32] [33] [34] [35]
16Balasubramanian Narasimhan [24]
17Winston Nelson [2]
18Dirk Ormoneit [12] [13] [28]
19Saharon Rosset [19] [20] [21] [22] [23] [25] [26]
20Y. Dan Rubinstein [9]
21Scott C. Schmidler [10] [11]
22Gavin Sherlock [14]
23Gongyi Shi [24]
24Patrice Y. Simard (Patrice Simard) [3]
25Scott Soltys [24]
26Robert Tibshirani [4] [5] [6] [8] [14] [16] [18] [20] [23] [24] [25] [36]
27Olga G. Troyanskaya [14]
28William Turin [2]
29Shivakumar Vaithyanathan [27]
30Haim J. Wolfson [1]
31Thomas D. Wu [10] [11]
32Ji Zhu [15] [17] [19] [20] [21] [22] [23] [25] [26]
33Hui Zou [26]

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Copyright © Sun May 17 03:24:02 2009 by Michael Ley (ley@uni-trier.de)