Cyril Goutte

NeurIPS Conference 2009 Conference Paper

Learning from Multiple Partially Observed Views - an Application to Multilingual Text Categorization

• Massih R. Amini
• Nicolas Usunier
• Cyril Goutte

We address the problem of learning classifiers when observations have multiple views, some of which may not be observed for all examples. We assume the existence of view generating functions which may complete the missing views in an approximate way. This situation corresponds for example to learning text classifiers from multilingual collections where documents are not available in all languages. In that case, Machine Translation (MT) systems may be used to translate each document in the missing languages. We derive a generalization error bound for classifiers learned on examples with multiple artificially created views. Our result uncovers a trade-off between the size of the training set, the number of views, and the quality of the view generating functions. As a consequence, we identify situations where it is more interesting to use multiple views for learning instead of classical single view learning. An extension of this framework is a natural way to leverage unlabeled multi-view data in semi-supervised learning. Experimental results on a subset of the Reuters RCV1/RCV2 collections support our findings by showing that additional views obtained from MT may significantly improve the classification performance in the cases identified by our trade-off.

ICML Conference 2006 Conference Paper

Categorization in multiple category systems

• Jean-Michel Renders
• Éric Gaussier
• Cyril Goutte
• François Pacull
• Gabriela Csurka

JMLR Journal 2003 Journal Article

Word-Sequence Kernels

• Nicola Cancedda
• Eric Gaussier
• Cyril Goutte
• Jean-Michel Renders

We address the problem of categorising documents using kernel-based methods such as Support Vector Machines. Since the work of Joachims (1998), there is ample experimental evidence that SVM using the standard word frequencies as features yield state-of-the-art performance on a number of benchmark problems. Recently, Lodhi et al. (2002) proposed the use of string kernels, a novel way of computing document similarity based of matching non-consecutive subsequences of characters. In this article, we propose the use of this technique with sequences of words rather than characters. This approach has several advantages, in particular it is more efficient computationally and it ties in closely with standard linguistic pre-processing techniques. We present some extensions to sequence kernels dealing with symbol-dependent and match-dependent decay factors, and present empirical evaluations of these extensions on the Reuters-21578 datasets.

YNIMG Journal 2001 Journal Article

Modelling the fMRI response using smooth FIR filters

• Finn Årup Nielsen
• Cyril Goutte
• Lars Kai Hansen

YNIMG Journal 1999 Journal Article

On Clustering fMRI Time Series

• Cyril Goutte
• Peter Toft
• Egill Rostrup
• Finn Å. Nielsen
• Lars Kai Hansen

Analysis of fMRI time series is often performed by extracting one or more parameters for the individual voxels. Methods based, e. g. , on various statistical tests are then used to yield parameters corresponding to probability of activation or activation strength. However, these methods do not indicate whether sets of voxels are activated in a similar way or in different ways. Typically, delays between two activated signals are not identified. In this article, we use clustering methods to detect similarities in activation between voxels. We employ a novel metric that measures the similarity between the activation stimulus and the fMRI signal. We present two different clustering algorithms and use them to identify regions of similar activations in an fMRI experiment involving a visual stimulus.