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Publications of Author

1. Bruno-Edouard Perrin, Liva Ralaivola, Aurélien Mazurie, Samuele Bottani, Jacques Mallet, Florence d'Alché-Buc
   Gene networks inference using dynamic Bayesian networks. [Citation Graph (0, 0)][DBLP]
   ECCB, 2003, pp:138-148 [Conf]
   
2. Liva Ralaivola, Lin Wu, Pierre Baldi
   SVM and pattern-enriched common fate graphs for the game of go. [Citation Graph (0, 0)][DBLP]
   ESANN, 2005, pp:485-490 [Conf]
   
3. Liva Ralaivola, Florence d'Alché-Buc
   Incremental Support Vector Machine Learning: A Local Approach. [Citation Graph (0, 0)][DBLP]
   ICANN, 2001, pp:322-330 [Conf]
   
4. François Denis, Christophe Nicolas Magnan, Liva Ralaivola
   Efficient learning of Naive Bayes classifiers under class-conditional classification noise. [Citation Graph (0, 0)][DBLP]
   ICML, 2006, pp:265-272 [Conf]
   
5. Liva Ralaivola, François Denis, Christophe Nicolas Magnan
   CN = CPCN. [Citation Graph (0, 0)][DBLP]
   ICML, 2006, pp:721-728 [Conf]
   
6. 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. [Citation Graph (0, 0)][DBLP]
   ISMB (Supplement of Bioinformatics), 2005, pp:359-368 [Conf]
   
7. Liva Ralaivola, Florence d'Alché-Buc
   Dynamical Modeling with Kernels for Nonlinear Time Series Prediction. [Citation Graph (0, 0)][DBLP]
   NIPS, 2003, pp:- [Conf]
   
8. Liva Ralaivola, Sanjay Joshua Swamidass, Hiroto Saigo, Pierre Baldi
   Graph kernels for chemical informatics. [Citation Graph (0, 0)][DBLP]
   Neural Networks, 2005, v:18, n:8, pp:1093-1110 [Journal]
   
9. Guillaume Stempfel, Liva Ralaivola
   Learning Kernel Perceptrons on Noisy Data Using Random Projections. [Citation Graph (0, 0)][DBLP]
   ALT, 2007, pp:328-342 [Conf]
   
10. 
    Learning SVMs from Sloppily Labeled Data. [Citation Graph (, )][DBLP]
    
    
11. 
    Grammatical inference as a principal component analysis problem. [Citation Graph (, )][DBLP]
    
    
12. 
    Multiple indefinite kernel learning with mixed norm regularization. [Citation Graph (, )][DBLP]
    
    
13. 
    Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary \beta-Mixing Processes [Citation Graph (, )][DBLP]