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Architectures neuronales pour l'approximation des fonctions de transfert: application à la télédétection = Neural network architectures for the approximation of transfer functions: application to remote sensingMejia Mantilla, Carlos; Fogelman, Françoise.1992, 208 p.Thesis

CeliClassifier: supervised learning of cellular phenotypesRÄMÖ, Pauli; SACHER, Raphael; SNIJDER, Berend et al.Bioinformatics (Oxford. Print). 2009, Vol 25, Num 22, pp 3028-3030, issn 1367-4803, 3 p.Article

Apprentissage supervisé et rappel d'images simples avec le Réseau Neuronal AléatoireHUBERT, C.Comptes rendus de l'Académie des sciences. Série 1, Mathématique. 1993, Vol 316, Num 1, pp 93-96, issn 0764-4442Article

The rise and fall of supervised machine learning techniquesJUHL JENSEN, Lars; BATEMAN, Alex.Bioinformatics (Oxford. Print). 2011, Vol 27, Num 24, pp 3331-3332, issn 1367-4803, 2 p.Article

Improved training rules for multilayered feedforward neural networksSU WHAN SUNG; LEE, Tai-Yong; PARK, Sunwon et al.Industrial & engineering chemistry research. 2003, Vol 42, Num 6, pp 1275-1278, issn 0888-5885, 4 p.Article

Traitements automatiques pour la migration de documents numériques vers XML = Automatic processing for digital document migration toward XMLFUSELIER, Jérome; CHIDLOVSKII, Boris.Document numérique. 2006, Vol 9, Num 1, pp 9-24, issn 1279-5127, 16 p.Article

An Unsupervised Automated Essay-Scoring SystemCHEN, Yen-Yu; LIU, Chien-Liang; LEE, Chia-Hoang et al.IEEE intelligent systems. 2010, Vol 25, Num 5, pp 61-67, issn 1541-1672, 7 p.Article

A critic-critic architecture to combine reinforcement and supervised learningsMONTAGNE, Fabien; DELEPOULLE, Samuel; PREUX, Philippe et al.European workshop on reinforcement learning. 2003, pp 13-14, isbn 2-7261-1259-5, 2 p.Conference Paper

Real-valued multiple-instance learning with queriesDOOLY, Daniel R; GOLDMAN, Sally A; KWEK, Stephen S et al.Lecture notes in computer science. 2001, pp 167-180, issn 0302-9743, isbn 3-540-42875-5Conference Paper

A closed-form reduction of multi-class cost-sensitive learning to weighted multi-class learningFEN XIA; YANG, Yan-Wu; LIANG ZHOU et al.Pattern recognition. 2009, Vol 42, Num 7, pp 1572-1581, issn 0031-3203, 10 p.Article

The bootstrapping of the Yarowsky algorithm in real corporaSANCHEZ-DE-MADARIAGA, Ricardo; FERNANDEZ-DEL-CASTILLO, José R.Information processing & management. 2009, Vol 45, Num 1, pp 55-69, issn 0306-4573, 15 p.Article

Application of feature selection for unsupervised learning in prosecutors' officePENG LIU; JIAXIAN ZHU; LANJUAN LIU et al.Lecture notes in computer science. 2005, issn 0302-9743, isbn 3-540-28312-9, 2Vol, Part II, 35-38Conference Paper

A neurobiologically motivated model for self-organized learningEMMERT-STREIB, Frank.Lecture notes in computer science. 2005, pp 415-424, issn 0302-9743, isbn 3-540-29896-7, 1Vol, 10 p.Conference Paper

The imbalanced training sample problem: Under or over sampling?BARANDELA, Ricardo; VALDOVINOS, Rosa M; SANCHEZ, J. Salvador et al.Lecture notes in computer science. 2004, pp 806-814, issn 0302-9743, isbn 3-540-22570-6, 9 p.Conference Paper

Apprentissage Supervisé pour la Généralisation Cartographique = Supervised Machine Learning for Cartographic GeneralisationMustière, Sébastien; Ganascia, Jean-Gabriel.2001, 241 p.Thesis

GA-based supervised learning of NeocognitronDAMING SHI; CHEW LIM TAN.IEEE-INNS-ENNS international joint conference on neural networks. 2000, pp Vol6.559-564, isbn 0-7695-0619-4, 6VolConference Paper

Apprentissage supervisé de règles de décision multiclasses avec contraintes de performances évolutives = Supervised learning of multiclass decision rules with evolutive performance constraintsJRAD, Nisrine; GRALL-MAES, Edith; BEAUSEROY, Pierre et al.Colloque sur le traitement du signal et des images. 2009, 1Vol, p. 93Conference Paper

Ordinal regression based subpixel shift estimation for video super-resolutionDAS GUPTA, Mithun; RAJARAM, Shyamsundar; HUANG, Thomas S et al.EURASIP Journal on Advances in Signal Processing (Print). 2007, Vol 2007, Num 18, issn 1687-6172, B1-B9Article

Refining hierarchical taxonomy structure via Semi-supervised LearningRUIZHANG HUANG; ZHIGANG ZHANG; LAM, Wai et al.International ACM SIGIR conference on research and development in information retrieval. 2006, pp 653-654, isbn 1-59593-369-7, 1Vol, 2 p.Conference Paper

Graph based semi-supervised learning with sharper edgesSHIN, Hyunjung; HILL, N. Jeremy; RÄTSCH, Gunnar et al.Lecture notes in computer science. 2006, pp 401-412, issn 0302-9743, isbn 3-540-45375-X, 1Vol, 12 p.Conference Paper

Data preprocessing and kappa coefficientLEGRAND, Gaelle; NICOLOYANNIS, Nicolas.Lecture notes in computer science. 2005, issn 0302-9743, isbn 3-540-28653-5, 2Vol, Part I, 176-184Conference Paper

Efficient face orientation discriminationBALUJA, Shumeet; SAHAMI, Mehran; ROWLEY, Henry A et al.International Conference on Image Processing. 2004, isbn 0-7803-8554-3, 5Vol, Vol1, 589-592Conference Paper

Generalisation and model selection in supervised learning with evolutionary computationROWLAND, Jem J.Lecture notes in computer science. 2003, pp 119-130, issn 0302-9743, isbn 3-540-00976-0, 12 p.Conference Paper

Using a modified counter-propagation algorithm to classify conjoint dataPIERROT, Hans; HENDTLASS, Tim.Lecture notes in computer science. 2003, pp 337-347, issn 0302-9743, isbn 3-540-40455-4, 11 p.Conference Paper

Learning classification with both labeled and unlabeled dataVITTAUT, Jean-Noël; AMINI, Massih-Reza; GALLINARI, Patrick et al.Lecture notes in computer science. 2002, pp 468-479, issn 0302-9743, isbn 3-540-44036-4, 12 p.Conference Paper

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