| 2006 |
|---|
| 10 | EE | Yasemin Altun,
Alexander J. Smola:
Unifying Divergence Minimization and Statistical Inference Via Convex Duality.
COLT 2006: 139-153 |
| 9 | EE | Quoc V. Le,
Alexander J. Smola,
Thomas Gärtner,
Yasemin Altun:
Transductive Gaussian Process Regression with Automatic Model Selection.
ECML 2006: 306-317 |
| 2005 |
|---|
| 8 | EE | Yasemin Altun,
David A. McAllester,
Mikhail Belkin:
Margin Semi-Supervised Learning for Structured Variables.
NIPS 2005 |
| 7 | EE | Ioannis Tsochantaridis,
Thorsten Joachims,
Thomas Hofmann,
Yasemin Altun:
Large Margin Methods for Structured and Interdependent Output Variables.
Journal of Machine Learning Research 6: 1453-1484 (2005) |
| 2004 |
|---|
| 6 | EE | Michelle Gregory,
Yasemin Altun:
Using Conditional Random Fields to Predict Pitch Accents in Conversational Speech.
ACL 2004: 677-683 |
| 5 | EE | Yasemin Altun,
Thomas Hofmann,
Alex J. Smola:
Gaussian process classification for segmenting and annotating sequences.
ICML 2004 |
| 4 | EE | Ioannis Tsochantaridis,
Thomas Hofmann,
Thorsten Joachims,
Yasemin Altun:
Support vector machine learning for interdependent and structured output spaces.
ICML 2004 |
| 3 | EE | Yasemin Altun,
Alexander J. Smola,
Thomas Hofmann:
Exponential Families for Conditional Random Fields.
UAI 2004: 2-9 |
| 2003 |
|---|
| 2 | | Yasemin Altun,
Ioannis Tsochantaridis,
Thomas Hofmann:
Hidden Markov Support Vector Machines.
ICML 2003: 3-10 |
| 2002 |
|---|
| 1 | EE | Yasemin Altun,
Thomas Hofmann,
Mark Johnson:
Discriminative Learning for Label Sequences via Boosting.
NIPS 2002: 977-984 |
