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Learning with tensors: a framework based on convex optimization and spectral regularizationSIGNORETTO, Marco; QUOC TRAN DINH; DE LATHAUWER, Lieven et al.Machine learning. 2014, Vol 94, Num 3, pp 303-351, issn 0885-6125, 49 p.Article

The Variational GarroteKAPPEN, Hilbert J; GOMEZ, Vicenç.Machine learning. 2014, Vol 96, Num 3, pp 269-294, issn 0885-6125, 26 p.Article

Special Issue on Learning SemanticsBORDES, Antoine; BOTTOU, Léon; COLLOBERT, Ronan et al.Machine learning. 2014, Vol 94, Num 2, issn 0885-6125, 176 p.Conference Proceedings

Spectral learning of weighted automata A forward-backward perspectiveBALLE, Borja; CARRERAS, Xavier; LUQUE, Franco M et al.Machine learning. 2014, Vol 96, Num 1-2, pp 33-63, issn 0885-6125, 31 p.Article

Modelling relational statistics with Bayes NetsSCHULTE, Oliver; KHOSRAVI, Hassan; KIRKPATRICK, Arthur E et al.Machine learning. 2014, Vol 94, Num 1, pp 105-125, issn 0885-6125, 21 p.Article

Imputation of missing links and attributes in longitudinal social surveysOUZIENKO, Vladimir; OBRADOVIC, Zoran.Machine learning. 2014, Vol 95, Num 3, pp 329-356, issn 0885-6125, 28 p.Article

Leave-one-out cross-validation is risk consistent for lassoHOMRIGHAUSEN, Darren; MCDONALD, Daniel J.Machine learning. 2014, Vol 97, Num 1-2, pp 65-78, issn 0885-6125, 14 p.Article

A theoretical and empirical analysis of support vector machine methods for multiple-instance classificationDORAN, Gary; RAY, Soumya.Machine learning. 2014, Vol 97, Num 1-2, pp 79-102, issn 0885-6125, 24 p.Article

Completing causal networks by meta-level abductionINOUE, Katsumi; DONCESCU, Andrei; NABESHIMA, Hidetomo et al.Machine learning. 2013, Vol 91, Num 2, pp 239-277, issn 0885-6125, 39 p.Article

Density estimation with minimization of U-divergenceNAITO, Kanta; EGUCHI, Shinto.Machine learning. 2013, Vol 90, Num 1, pp 29-57, issn 0885-6125, 29 p.Article

Ranking data with ordinal labels: optimality and pairwise aggregationCLEMENCON, Stéphan; ROBBIANO, Sylvain; VAYATIS, Nicolas et al.Machine learning. 2013, Vol 91, Num 1, pp 67-104, issn 0885-6125, 38 p.Article

Semi-supervised learning with density-ratio estimationKAWAKITA, Masanori; KANAMORI, Takafumi.Machine learning. 2013, Vol 91, Num 2, pp 189-209, issn 0885-6125, 21 p.Article

Correlated topographic analysis: estimating an ordering of correlated componentsSASAKI, Hiroaki; GUTMANN, Michael U; SHOUNO, Hayaru et al.Machine learning. 2013, Vol 92, Num 2-3, pp 285-317, issn 0885-6125, 33 p.Article

Hypervolume indicator and dominance reward based multi-objective Monte-Carlo Tree SearchWEIJIA WANG; SEBAG, Michèle.Machine learning. 2013, Vol 92, Num 2-3, pp 403-429, issn 0885-6125, 27 p.Article

Multi-stage classifier designTRAPEZNIKOV, Kirill; SALIGRAMA, Venkatesh; CASTAÑÓN, David et al.Machine learning. 2013, Vol 92, Num 2-3, pp 479-502, issn 0885-6125, 24 p.Article

Calibration and regret bounds for order-preserving surrogate losses in learning to rankCALAUZENES, Clément; USUNIER, Nicolas; GALLINARI, Patrick et al.Machine learning. 2013, Vol 93, Num 2-3, pp 227-260, issn 0885-6125, 34 p.Article

A comparative evaluation of stochastic-based inference methods for Gaussian process modelsFILIPPONE, M; ZHONG, M; GIROLAMI, M et al.Machine learning. 2013, Vol 93, Num 1, pp 93-114, issn 0885-6125, 22 p.Conference Paper

Pairwise meta-rules for better meta-learning-based algorithm rankingQUAN SUN; PFAHRINGER, Bernhard.Machine learning. 2013, Vol 93, Num 1, pp 141-161, issn 0885-6125, 21 p.Conference Paper

Multi-parametric solution-path algorithm for instance-weighted support vector machinesKARASUYAMA, Masayuki; HARADA, Naoyuki; SUGIYAMA, Masashi et al.Machine learning. 2012, Vol 88, Num 3, pp 297-330, issn 0885-6125, 34 p.Article

Online variance minimizationWARMUTH, Manfred K; KUZMIN, Dima.Machine learning. 2012, Vol 87, Num 1, pp 1-32, issn 0885-6125, 32 p.Article

ROC convex hull and nonparametric maximum likelihood estimationLIM, Johan; WON, Joong-Ho.Machine learning. 2012, Vol 88, Num 3, pp 433-444, issn 0885-6125, 12 p.Article

Robustness and generalizationHUAN XU; MANNOR, Shie.Machine learning. 2012, Vol 86, Num 3, pp 391-423, issn 0885-6125, 33 p.Article

Subsumption resolution: an efficient and effective technique for semi-naive Bayesian learningFEI ZHENG; WEBB, Geoffrey I; SURAWEERA, Pramuditha et al.Machine learning. 2012, Vol 87, Num 1, pp 93-125, issn 0885-6125, 33 p.Article

Temporal-difference search in computer GoSILVER, David; SUTTON, Richard S; MÜLLER, Martin et al.Machine learning. 2012, Vol 87, Num 2, pp 183-219, issn 0885-6125, 37 p.Article

Compressed labeling on distilled labelsets for multi-label learningTIANYI ZHOU; DACHENG TAO; XINDONG WU et al.Machine learning. 2012, Vol 88, Num 1-2, pp 69-126, issn 0885-6125, 58 p.Article

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