| 2007 |
|---|
| 9 | EE | Guillaume Stempfel,
Liva Ralaivola:
Learning Kernel Perceptrons on Noisy Data Using Random Projections.
ALT 2007: 328-342 |
| 2006 |
|---|
| 8 | EE | François Denis,
Christophe Nicolas Magnan,
Liva Ralaivola:
Efficient learning of Naive Bayes classifiers under class-conditional classification noise.
ICML 2006: 265-272 |
| 7 | EE | Liva Ralaivola,
François Denis,
Christophe Nicolas Magnan:
CN = CPCN.
ICML 2006: 721-728 |
| 2005 |
|---|
| 6 | EE | Liva Ralaivola,
Lin Wu,
Pierre Baldi:
SVM and pattern-enriched common fate graphs for the game of go.
ESANN 2005: 485-490 |
| 5 | EE | Sanjay Joshua Swamidass,
Jonathan H. Chen,
Jocelyne Bruand,
Peter Phung,
Liva Ralaivola,
Pierre Baldi:
Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity.
ISMB (Supplement of Bioinformatics) 2005: 359-368 |
| 4 | EE | Liva Ralaivola,
Sanjay Joshua Swamidass,
Hiroto Saigo,
Pierre Baldi:
Graph kernels for chemical informatics.
Neural Networks 18(8): 1093-1110 (2005) |
| 2003 |
|---|
| 3 | | Bruno-Edouard Perrin,
Liva Ralaivola,
Aurélien Mazurie,
Samuele Bottani,
Jacques Mallet,
Florence d'Alché-Buc:
Gene networks inference using dynamic Bayesian networks.
ECCB 2003: 138-148 |
| 2 | EE | Liva Ralaivola,
Florence d'Alché-Buc:
Dynamical Modeling with Kernels for Nonlinear Time Series Prediction.
NIPS 2003 |
| 2001 |
|---|
| 1 | EE | Liva Ralaivola,
Florence d'Alché-Buc:
Incremental Support Vector Machine Learning: A Local Approach.
ICANN 2001: 322-330 |
