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Within machine learning, the supervised learning field aims at modeling the input-output relationship of a system, from past observations of its behavior.
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Multi-output process identification
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Improving regressors using boosting techniques
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A decision-theoretic generalization of on-line learning and an application to boosting
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On bias, variance, 0/1—loss, and the curse-of-dimensionality
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First order regression
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Kernel principal component analysis
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An empirical comparison of voting classification algorithms: Bagging, boosting, and variants
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Multiclass learning, boosting, and error-correcting codes
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Probabilistic kernel regression models
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Improved boosting algorithms using confidence-rated predictions
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An evaluation of statistical approaches to text categorization
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Top-down induction of clustering trees
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Predicting chemical parameters of river water quality from bioindicator data
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Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors)
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Some enhancements of decision tree bagging
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Boostexter: A boosting-based system for text categorization
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A kernel method for multi-labelled classification
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Greedy function approximation: a gradient boosting machine
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Improving multiclass text classification with the support vector machine
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Learning with kernels: support vector machines, regularization, optimization, and beyond
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Analyzing bagging
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Logistic regression, adaboost and bregman distances
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Multivariate regression trees: a new technique for modeling species–environment relationships
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Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary
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Stochastic gradient boosting
J. H. Friedman · 2002
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Contributions to decision tree induction: bias/variance tradeoff and time series classification
P. Geurts · 2002
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Principal component analysis
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Database-friendly random projections: Johnson-lindenstrauss with binary coins
D. Achlioptas · 2003
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Task clustering and gating for bayesian multitask learning
B. Bakker and T. Heskes · 2003
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Machine learning and data mining for yeast functional genomics
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A practical guide to support vector classification, 2003
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Variance and bias for general loss functions
G. M. James · 2003
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Multi-output support vector regression
E. Vazquez and E. Walter · 2003
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Learning multi-label scene classification
M. R. Boutell, J. Luo, X. Shen, and C. M. Brown · 2004
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Is cross-validation valid for small-sample microarray classification?
U. M. Braga-Neto and E. R. Dougherty · 2004
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Using random forest to learn imbalanced data
C. Chen, A. Liaw, and L. Breiman · 2004
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Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning
A. Rahimi and B. Recht · 2009
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Random projection ensemble classifiers
A. Schclar and L. Rokach · 2009
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A systematic analysis of performance measures for classification tasks
M. Sokolova and G. Lapalme · 2009
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Mining multi-label data
G. Tsoumakas, I. Katakis, and I. Vlahavas · 2009
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Ml-rbf: Rbf neural networks for multi-label learning
M.-L. Zhang · 2009
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Multi-class adaboost
J. Zhu, H. Zou, S. Rosset, and T. Hastie · 2009
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T. Evgeniou and M. Pontil · 2004
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Discriminative methods for multi-labeled classification
S. Godbole and S. Sarawagi · 2004
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The enron corpus: A new dataset for email classification research
B. Klimt and Y. Yang · 2004
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Learning methods for generic object recognition with invariance to pose and lighting
Y. LeCun, F. J. Huang, and L. Bottou · 2004
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An unbiased method for constructing multilabel classification trees
H. G. Noh, M. S. Song, and S. H. Park · 2004
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Svm multiregression for nonlinear channel estimation in multiple-input multiple-output systems
M. Sánchez-Fernández, M. de Prado-Cumplido, J. Arenas-García, and F. Pérez-Cruz · 2004
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Bayes optimal multilabel classification via probabilistic classifier chains
W. Cheng, E. Hüllermeier, and K. J. Dembczynski · 2010
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Multiclass-multilabel classification with more classes than examples
O. Dekel and O. Shamir · 2010
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On label dependence in multi-label classification
K. Dembczynski, W. Waegeman, W. Cheng, and E. Hüllermeier · 2010
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Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement
G. Forman and M. Scholz · 2010
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Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
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Node harvest
N. Meinshausen · 2010
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Efficient multilabel classification algorithms for large-scale problems in the legal domain
E. L. Mencía and J. Fürnkranz · 2010
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Multi-label learning by exploiting label dependency
M.-L. Zhang and K. Zhang · 2010
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An experimental comparison of cross-validation techniques for estimating the area under the roc curve
A. Airola, T. Pahikkala, W. Waegeman, B. De Baets, and T. Salakoski · 2011
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Algorithms for hyper-parameter optimization
J. S. Bergstra, R. Bardenet, Y. Bengio, and B. Kégl · 2011
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A probabilistic and ripless theory of compressed sensing
E. J. Candes and Y. Plan · 2011
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Multi-label classification with error-correcting codes
C.-S. Ferng and H.-T. Lin · 2011
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Sequential model-based optimization for general algorithm configuration
F. Hutter, H. H. Hoos, and K. Leyton-Brown · 2011
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On oblique random forests
B. H. Menze, B. M. Kelm, D. N. Splitthoff, U. Koethe, and F. A. Hamprecht · 2011
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Active learning using on-line algorithms
C. Mesterharm and M. J. Pazzani · 2011
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Scikit-learn: Machine learning in python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, et al · 2011
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Classifier chains for multi-label classification
J. Read, B. Pfahringer, G. Holmes, and E. Frank · 2011
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Multivariate random forests
M. Segal and Y. Xiao · 2011
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Random forest based feature induction
C. Vens and F. Costa · 2011
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Extracting sets of chemical substructures and protein domains governing drug-target interactions
Y. Yamanishi, E. Pauwels, H. Saigo, and V. Stoven · 2011
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A dependent multilabel classification method derived from the k-nearest neighbor rule
Z. Younes, F. Abdallah, T. Denoeux, and H. Snoussi · 2011
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Multi-label output codes using canonical correlation analysis
Y. Zhang and J. G. Schneider · 2011
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Multi-output learning via spectral filtering
L. Baldassarre, L. Rosasco, A. Barla, and A. Verri · 2012
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Practical recommendations for gradient-based training of deep architectures
Y. Bengio · 2012
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Random search for hyper-parameter optimization
J. Bergstra and Y. Bengio · 2012
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Least absolute deviations: Theory, applications and algorithms , volume 6
P. Bloomfield and W. Steiger · 2012
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Stochastic gradient descent tricks
L. Bottou · 2012
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N. Boulanger-Lewandowski, Y. Bengio, and P. Vincent · 2012
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A ranking-based knn approach for multi-label classification
T.-H. Chiang, H.-Y. Lo, and S.-D. Lin · 2012
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Hierarchical classification of diatom images using ensembles of predictive clustering trees
I. Dimitrovski, D. Kocev, S. Loskovska, and S. Džeroski · 2012
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Multi-label hypothesis reuse
S.-J. Huang, Y. Yu, and Z.-H. Zhou · 2012
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L1-based compression of random forest models
A. Joly, F. Schnitzler, P. Geurts, and L. Wehenkel · 2012
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Multi-label classification using error correcting output codes
T. Kajdanowicz and P. Kazienko · 2012
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Multilabel classification using bayesian compressed sensing
A. Kapoor, R. Viswanathan, and P. Jain · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Efficient backprop
Y. A. LeCun, L. Bottou, G. B. Orr, and K.-R. Müller · 2012
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Ensembles on random patches
G. Louppe and P. Geurts · 2012
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An extensive experimental comparison of methods for multi-label learning
G. Madjarov, D. Kocev, D. Gjorgjevikj, and S. Džeroski · 2012
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Practical bayesian optimization of machine learning algorithms
J. Snoek, H. Larochelle, and R. P. Adams · 2012
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An efficient multi-label support vector machine with a zero label
J. Xu · 2012
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Multi-label subspace ensemble
T. Zhou and D. Tao · 2012
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Ensemble methods: foundations and algorithms
Z.-H. Zhou · 2012
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Multi-label learning with millions of labels: Recommending advertiser bid phrases for web pages
R. Agrawal, A. Gupta, Y. Prabhu, and M. Varma · 2013
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Algorithms for minimization without derivatives
R. P. Brent · 2013
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The 9th annual mlsp competition: New methods for acoustic classification of multiple simultaneous bird species in a noisy environment
F. Briggs, Y. Huang, R. Raich, K. Eftaxias, Z. Lei, W. Cukierski, S. F. Hadley, A. Hadley, M. Betts, X. Z. Fern, et al · 2013
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Api design for machine learning software: experiences from the scikit-learn project
L. Buitinck, G. Louppe, M. Blondel, F. Pedregosa, A. Mueller, O. Grisel, V. Niculae, P. Prettenhofer, A. Gramfort, J. Grobler, et al · 2013
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Robust bloom filters for large multilabel classification tasks
M. M. Cisse, N. Usunier, T. Artieres, and P. Gallinari · 2013
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Speech recognition with deep recurrent neural networks
A. Graves, A.-r. Mohamed, and G. Hinton · 2013
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Tree ensembles for predicting structured outputs
D. Kocev, C. Vens, J. Struyf, and S. Džeroski · 2013
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UCI machine learning repository, 2013
M. Lichman · 2013
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Multi-output least-squares support vector regression machines
S. Xu, X. An, X. Qiao, L. Zhu, and L. Li · 2013
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Do we need hundreds of classifiers to solve real world classification problems?
M. Fernández-Delgado, E. Cernadas, S. Barro, and D. Amorim · 2014
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Multi-label learning: a review of the state of the art and ongoing research
E. Gibaja and S. Ventura · 2014
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Random forests with random projections of the output space for high dimensional multi-label classification
A. Joly, P. Geurts, and L. Wehenkel · 2014
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Randomized nonlinear component analysis
D. Lopez-Paz, S. Sra, A. J. Smola, Z. Ghahramani, and B. Schölkopf · 2014
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Understanding Random Forests: From Theory to Practice
G. Louppe · 2014
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Large-scale multi-label text classification—revisiting neural networks
J. Nam, J. Kim, E. L. Mencía, I. Gurevych, and J. Fürnkranz · 2014
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Fastxml: A fast, accurate and stable tree-classifier for extreme multi-label learning
Y. Prabhu and M. Varma · 2014
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Simple connectome inference from partial correlation statistics in calcium imaging
A. Sutera, A. Joly, V. François-Lavet, Z. A. Qiu, G. Louppe, D. Ernst, and P. Geurts · 2014
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Multi-target regression via random linear target combinations
G. Tsoumakas, E. Spyromitros-Xioufis, A. Vrekou, and I. Vlahavas · 2014
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A review on multi-label learning algorithms
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Random rotation ensembles
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A survey on multi-output regression
H. Borchani, G. Varando, C. Bielza, and P. Larrañaga · 2015
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On the optimality of multi-label classification under subset zero-one loss for distributions satisfying the composition property
M. Gasse, A. Aussem, and H. Elghazel · 2015
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A review on evaluation metrics for data classification evaluations
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Deep learning
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Bayesian and empirical bayesian forests
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Global refinement of random forest
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T. M. Tomita, M. Maggioni, and J. T. Vogelstein · 2015
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Joint learning and pruning of decision forests
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Impact of subsampling and pruning on random forests
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Multi-label classification methods for multi-target regression
E. Spyromitros-Xioufis, G. Tsoumakas, W. Groves, and I. Vlahavas · 2016
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