#### Filter Results:

- Full text PDF available (36)

#### Publication Year

1994

2017

- This year (2)
- Last 5 years (19)
- Last 10 years (28)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Data Set Used

#### Key Phrases

#### Method

#### Organism

Learn More

This article deals with the identification of gene regulatory networks from experimental data using a statistical machine learning approach. A stochastic model of gene interactions capable of handling missing variables is proposed. It can be described as a dynamic Bayesian network particularly well suited to tackle the stochastic nature of gene regulation… (More)

- Minh Quach, Nicolas Brunel, Florence d'Alché-Buc
- Bioinformatics
- 2007

MOTIVATION
Statistical inference of biological networks such as gene regulatory networks, signaling pathways and metabolic networks can contribute to build a picture of complex interactions that take place in the cell. However, biological systems considered as dynamical, non-linear and generally partially observed processes may be difficult to estimate even… (More)

In many discrimination problems a large amount of data is available but only a few of them are labeled. This provides a strong motivation to improve or develop methods for semi-supervised learning. In this paper, boosting is generalized to this task within the optimization framework of MarginBoost . We extend the margin definition to unlabeled data and… (More)

- Pierre Geurts, Louis Wehenkel, Florence d'Alché-Buc
- ICML
- 2006

We extend tree-based methods to the prediction of structured outputs using a kernelization of the algorithm that allows one to grow trees as soon as a kernel can be defined on the output space. The resulting algorithm, called output kernel trees (OK3), generalizes classification and regression trees as well as tree-based ensemble methods in a principled… (More)

- Liva Ralaivola, Florence d'Alché-Buc
- NIPS
- 2003

We consider the question of predicting nonlinear time series. Kernel Dynamical Modeling (KDM), a new method based on kernels, is proposed as an extension to linear dynamical models. The kernel trick is used twice: first, to learn the parameters of the model, and second, to compute preimages of the time series predicted in the feature space by means of… (More)

- Liva Ralaivola, Florence d'Alché-Buc
- ICANN
- 2001

In this paper, we propose and study a new on-line algorithm for learning a SVM based on Radial Basis Function Kernel: Local Incremental Learning of SVM or LISVM. Our method exploits the “locality” of RBF kernels to update current machine by only considering a subset of support candidates in the neighbourhood of the input. The determination of this subset is… (More)

- Pierre Geurts, Nizar Touleimat, Marie Dutreix, Florence d'Alché-Buc
- BMC Bioinformatics
- 2007

Elucidating biological networks between proteins appears nowadays as one of the most important challenges in systems biology. Computational approaches to this problem are important to complement high-throughput technologies and to help biologists in designing new experiments. In this work, we focus on the completion of a biological network from various… (More)

Link prediction is addressed as an output kernel learning task through semi-supervised Output Kernel Regression. Working in the framework of RKHS theory with vectorvalued functions, we establish a new representer theorem devoted to semi-supervised least square regression. We then apply it to get a new model (POKR: Penalized Output Kernel Regression) and… (More)

- Pierre Geurts, Louis Wehenkel, Florence d'Alché-Buc
- ICML
- 2007

A general framework is proposed for gradient boosting in supervised learning problems where the loss function is defined using a kernel over the output space. It extends boosting in a principled way to complex output spaces (images, text, graphs etc.) and can be applied to a general class of base learners working in kernelized output spaces. Empirical… (More)

- George Siolas, Florence d'Alché-Buc
- IJCNN
- 2000

We propose to solve a text categorization task using a new metric between documents, based on a priori semantic knowledge about words. This metric can be incorporated into the definition of radial basis kernels of Support Vector Machines or directly used in a K-nearest neighbors algorithm. Both SVM and KNN are tested and compared on the 20 newsgroups… (More)