2005.bib

@comment{{This file has been generated by bib2bib 1.98}}
@comment{{Command line: bib2bib -ob ./bibs/2005.bib -c year=2005 mesPublis.bib}}
@inproceedings{loosli2005invariances,
  title = {Invariances in classification: an efficient svm implementation},
  author = {Loosli, Gaëlle and Canu, Stéphane and Vishwanathan, SVN and Smola, Alex},
  year = {2005},
  booktitle = {Applied Stochastic Models and Data Analysis, Brest, France },
  abstract = {Often, in pattern recognition, complementary knowledge is available. This could be useful to improve the
  performance of the recognition system. Part of this knowledge regards invariances, in particular
  when treating images or voice data.
  Many approaches have been proposed to incorporate invariances in pattern recognition systems. Some of
  these approaches require a pre-processing phase, others integrate the invariances in the
  algorithms. We present a unifying formulation of the problem of incorporating invariances into a pattern recognition classifier and we extend the SimpleSVM algorithm  to handle invariances efficiently.}
}
@inproceedings{loosli2005context,
  title = {Context changes detection by one-class SVMs},
  author = {Loosli, Gaëlle and Lee, Sang-Goog and Canu, Stéphane},
  booktitle = {Proceedings Workshop on Machine Learning for User Modeling: Challenges},
  year = {2005},
  abstract = {We are interested in a low level part of user modeling, which is the change detection in the context of the user. We present a method to detect on line changes in the context of a user equipped with non invasive sensors. Our point is to provide, in real time, series of unlabeled contexts that can be classified and analyzed on a higher level. }
}
@inproceedings{loosli2005contextRetrieval,
  title = {Context retrieval by rupture detection},
  author = {Loosli, Gaëlle and Lee, Sans-Goog and Canu, Stéphane},
  year = {2005},
  book = {Conférence francophone sur l'apprentissage automatique , Nice, France},
  abstract = {This paper presents an algorithm for abrupt changes detection in signals, applied to physiological data. We are working on automatic context detection through wearable computers and clothes integrated sensors. Among the many problems that exists in this area, we are particularly interested in the automatic detection of changes in the state of the user, in order to develop some {\em context aware} applications. For this purpose, the algorithm described here is based on one class support vector machines. We also illustrates our algorithm on two experiments in which data are collected through biological sensors and accelerometers.}
}
@inproceedings{LoosliLC05,
  author = {Gaëlle Loosli and Sang{-}Goog Lee and Stéphane Canu},
  title = {Rupture detection for context aware applications},
  booktitle = {Proceedings of the First Internaltional Workshop on Personalized Context
	Modeling and Management for UbiComp Applications, ubiPCMM 2005, Tokyo,
	Japan, September 11, 2005.},
  year = {2005},
  url = {http://ceur-ws.org/Vol-149/paper12.pdf},
  abstract = {Automatic context detection through wearable computers and sensors integrated in clothes is the question we address in this paper. Among the many problems that exists in this area, we are particularly interested in the automatic detection of changes in the state of the user, in order to developpe some {\em context aware} applications. This paper presents a machine learning method for rupture detection, based on one class support vector machines. It also illustrates our algorithm on two experiments in which data are collected by biological sensors and accelerometers. }
}
@inproceedings{LoosliLSC05,
  author = {Gaële Loosli and
	Sans{-}Goog Lee and
	Stéphane Canu},
  title = {Context Retrieval by Rupture Detection},
  booktitle = {Actes de {CAP} 05, Conférence francophone sur l'apprentissage
	automatique - 2005, Nice, France, du 31 mai au 3 juin 2005},
  pages = {111--112},
  year = {2005},
  abstract = {This paper presents an algorithm for abrupt changes detection in signals, applied to physiological data. We are working on automatic context detection through wearable computers and clothes integrated sensors. Among the many problems that exists in this area, we are particularly interested in the automatic detection of changes in the state of the user, in order to develop some {\em context aware} applications. For this purpose, the algorithm described here is based on one class support vector machines. We also illustrates our algorithm on two experiments in which data are collected through biological sensors and accelerometers.}
}
@inproceedings{loosli2005lasvm,
  title = {LASVM applied to invariant problems},
  author = {Loosli, Gaëlle},
  year = {2005},
  book = {NIPS workshop on Large Scale Kernel Machines}
}
@article{LoosliCVSC05,
  author = {Gaëlle Loosli and
	Stéphane Canu and
	S. V. N. Vishwanathan and
	Alexander J. Smola and
	M. Chattopadhyay},
  title = {Boïte à outils {SVM} simple et rapide},
  journal = {Revue d'Intelligence Artificielle},
  volume = {19},
  number = {4-5},
  pages = {741--767},
  year = {2005},
  url = {https://doi.org/10.3166/ria.19.741-767},
  doi = {10.3166/ria.19.741-767},
  timestamp = {Sat, 27 May 2017 14:23:00 +0200},
  abstract = {If SVM (Support Vector Machines) are now considered as one of the best learning
	methods, they are still considered as slow. Here we propose a Matlab toolbox that enables the
	usage of SVM in a fast and simple way. This is done thanks to the projected gradient method
	which is well adapted to the problem : SimpleSVM. We chose to implement this
	algorithm with Matlab environment since it is user-friendly and efficient - it uses the ATLAS
	(Automatically Tuned Linear Algebra Software) library. The comparison to the state of the art
	in this field, SMO (Sequential Minimal Optimization) shows that in some cases, our solution is
	faster and less complex. In order to point out how fast and simple our method is, we give here
	results on the MNITS database. It was possible to compute a satisfying solution in a quite short
	time (one hour and a half on a PC with Linux distribution to compute 45 binary classifiers, with
	60000 samples in dimension 576). Moreover, we show how this algorithm can be extended to
	problems like invariances, breaking down into small pieces the problem such that it is possible
	to get the solution running only once the Invariant SimpleSVM.}
}