2008 |
34 | EE | Ron Bekkerman,
Koby Crammer:
One-Class Clustering in the Text Domain.
EMNLP 2008: 41-50 |
33 | EE | Mark Dredze,
Koby Crammer:
Online Methods for Multi-Domain Learning and Adaptation.
EMNLP 2008: 689-697 |
32 | EE | Koby Crammer,
Partha Pratim Talukdar,
Fernando Pereira:
A rate-distortion one-class model and its applications to clustering.
ICML 2008: 184-191 |
31 | EE | Mark Dredze,
Koby Crammer,
Fernando Pereira:
Confidence-weighted linear classification.
ICML 2008: 264-271 |
30 | EE | Koby Crammer,
Mark Dredze,
Fernando Pereira:
Exact Convex Confidence-Weighted Learning.
NIPS 2008: 345-352 |
29 | EE | Partha Pratim Talukdar,
Marie Jacob,
Muhammad Salman Mehmood,
Koby Crammer,
Zachary G. Ives,
Fernando Pereira,
Sudipto Guha:
Learning to create data-integrating queries.
PVLDB 1(1): 785-796 (2008) |
2007 |
28 | EE | John Blitzer,
Koby Crammer,
Alex Kulesza,
Fernando Pereira,
Jennifer Wortman:
Learning Bounds for Domain Adaptation.
NIPS 2007 |
2006 |
27 | | Linli Xu,
Koby Crammer,
Dale Schuurmans:
Robust Support Vector Machine Training via Convex Outlier Ablation.
AAAI 2006 |
26 | EE | Koby Crammer:
Online Tracking of Linear Subspaces.
COLT 2006: 438-452 |
25 | EE | Shai Ben-David,
John Blitzer,
Koby Crammer,
Fernando Pereira:
Analysis of Representations for Domain Adaptation.
NIPS 2006: 137-144 |
24 | EE | Koby Crammer,
Michael J. Kearns,
Jennifer Wortman:
Learning from Multiple Sources.
NIPS 2006: 321-328 |
23 | EE | Koby Crammer,
Amir Globerson:
Discriminative Learning via Semidefinite Probabilistic Models.
UAI 2006 |
22 | EE | Koby Crammer,
Ofer Dekel,
Joseph Keshet,
Shai Shalev-Shwartz,
Yoram Singer:
Online Passive-Aggressive Algorithms.
Journal of Machine Learning Research 7: 551-585 (2006) |
2005 |
21 | EE | Ryan T. McDonald,
Koby Crammer,
Fernando C. N. Pereira:
Online Large-Margin Training of Dependency Parsers.
ACL 2005 |
20 | EE | Koby Crammer,
Yoram Singer:
Loss Bounds for Online Category Ranking.
COLT 2005: 48-62 |
19 | EE | Ryan T. McDonald,
Koby Crammer,
Fernando Pereira:
Flexible Text Segmentation with Structured Multilabel Classification.
HLT/EMNLP 2005 |
18 | EE | Koby Crammer,
Michael S. Kearns,
Jennifer Wortman:
Learning from Data of Variable Quality.
NIPS 2005 |
17 | EE | Koby Crammer,
Yoram Singer:
Online Ranking by Projecting.
Neural Computation 17(1): 145-175 (2005) |
2004 |
16 | EE | Koby Crammer,
Gal Chechik:
A needle in a haystack: local one-class optimization.
ICML 2004 |
15 | EE | Lavi Shpigelman,
Koby Crammer,
Rony Paz,
Eilon Vaadia,
Yoram Singer:
A Temporal Kernel-Based Model for Tracking Hand Movements from Neural Activities.
NIPS 2004 |
2003 |
14 | EE | Koby Crammer,
Yoram Singer:
Learning Algorithm for Enclosing Points in Bregmanian Spheres.
COLT 2003: 388-402 |
13 | EE | Koby Crammer,
Jaz S. Kandola,
Yoram Singer:
Online Classification on a Budget.
NIPS 2003 |
12 | EE | Shai Shalev-Shwartz,
Koby Crammer,
Ofer Dekel,
Yoram Singer:
Online Passive-Aggressive Algorithms.
NIPS 2003 |
11 | EE | Koby Crammer,
Yoram Singer:
A Family of Additive Online Algorithms for Category Ranking.
Journal of Machine Learning Research 3: 1025-1058 (2003) |
10 | EE | Koby Crammer,
Yoram Singer:
Ultraconservative Online Algorithms for Multiclass Problems.
Journal of Machine Learning Research 3: 951-991 (2003) |
2002 |
9 | EE | Koby Crammer,
Ran Gilad-Bachrach,
Amir Navot,
Naftali Tishby:
Margin Analysis of the LVQ Algorithm.
NIPS 2002: 462-469 |
8 | EE | Koby Crammer,
Joseph Keshet,
Yoram Singer:
Kernel Design Using Boosting.
NIPS 2002: 537-544 |
7 | EE | Koby Crammer,
Yoram Singer:
A new family of online algorithms for category ranking.
SIGIR 2002: 151-158 |
6 | | Koby Crammer,
Yoram Singer:
On the Learnability and Design of Output Codes for Multiclass Problems.
Machine Learning 47(2-3): 201-233 (2002) |
2001 |
5 | EE | Koby Crammer,
Yoram Singer:
Ultraconservative Online Algorithms for Multiclass Problems.
COLT/EuroCOLT 2001: 99-115 |
4 | EE | Koby Crammer,
Yoram Singer:
Pranking with Ranking.
NIPS 2001: 641-647 |
3 | EE | Koby Crammer,
Yoram Singer:
On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines.
Journal of Machine Learning Research 2: 265-292 (2001) |
2000 |
2 | | Koby Crammer,
Yoram Singer:
On the Learnability and Design of Output Codes for Multiclass Problems.
COLT 2000: 35-46 |
1 | | Koby Crammer,
Yoram Singer:
Improved Output Coding for Classification Using Continuous Relaxation.
NIPS 2000: 437-443 |