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Knowledge-Based Systems, ISSN 0950-7051, 2012, Volume 26, pp. 239 - 245
Numerous algorithms have been proposed to improve Naive Bayes (NB) by weakening its conditional attribute independence assumption, among which Tree Augmented... 
Conditional log likelihood | Naive Bayes | Tree Augmented Naive Bayes | Ensemble learning | Class probability estimation | AREA | ONE-DEPENDENCE ESTIMATORS | ROC CURVE | RANKING | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | CLASSIFIERS | Computer science | Advertising campaigns | Algorithms
Journal Article
Journal of the American Statistical Association, ISSN 0162-1459, 03/2010, Volume 105, Issue 489, pp. 424 - 436
Journal Article
Pattern Recognition, ISSN 0031-3203, 2011, Volume 44, Issue 7, pp. 1552 - 1565
Supervised classification based on error-correcting output codes (ECOC) is an efficient method to solve the problem of multi-class classification, and how to... 
Error-correcting output codes | Multi-class classification | Probability estimation | CLASSIFICATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Coding | Data sets | Classification | Criteria | Error correction | Pattern recognition | Bayesian analysis | Estimates
Journal Article
Biometrical Journal, ISSN 0323-3847, 03/2014, Volume 56, Issue 2, pp. 256 - 269
Journal Article
Neurocomputing, ISSN 0925-2312, 07/2014, Volume 136, pp. 30 - 40
Semi-supervised learning is a popular machine learning technique where only a small number of labeled examples are available and a large pool of unlabeled... 
Data editing | Co-training | Class probability | Ensemble learning | Semi-supervised learning | Distance metric | DECISION TREES | 3-D OBJECT RETRIEVAL | EXPLOITING UNLABELED DATA | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Noise control | Learning | Classifiers | Ranking | Noise | Machine learning | Editing | Performance enhancement | Pools
Journal Article
Journal of Machine Learning Research, ISSN 1532-4435, 03/2007, Volume 8, pp. 409 - 439
The standard by which binary classifiers are usually judged, misclassification error, assumes equal costs of misclassifying the two classes or, equivalently,... 
Under-sampling | AdaBoost | Boosting algorithms | Over-sampling | Stratification | LogitBoost | Class probability estimation | Data jittering | CONSISTENCY | stratification | data jittering | over-sampling | under-sampling | boosting algorithms | class probability estimation | AUTOMATION & CONTROL SYSTEMS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Journal Article
International Journal of Pattern Recognition and Artificial Intelligence, ISSN 0218-0014, 06/2009, Volume 23, Issue 4, pp. 745 - 763
Journal Article
Advances in Intelligent Systems and Computing, ISSN 2194-5357, 2015, Volume 339, pp. 343 - 351
Conference Proceeding
Information Sciences, ISSN 0020-0255, 01/2013, Volume 218, pp. 146 - 164
Class distribution estimation (quantification) plays an important role in many practical classification problems. Firstly, it is important in order to adapt... 
Hellinger distance | Class prior probability estimation | Quantification | Class distribution shift | IMAGE-ANALYSIS | COMPUTER SCIENCE, INFORMATION SYSTEMS | CLASSIFICATION | CLASSIFIERS | Neural networks
Journal Article
Statistical Methodology, ISSN 1572-3127, 2011, Volume 8, Issue 1, pp. 56 - 67
Many large-margin classifiers such as the Support Vector Machine (SVM) sidestep estimating conditional class probabilities and target the discovery of... 
Large-margin classifier | Conditional class probability | SVM | Preconditioning | SUPPORT VECTOR MACHINES | LOGISTIC-REGRESSION | CLASSIFICATION | STATISTICS & PROBABILITY | SELECTION | large-margin classifier | preconditioning
Journal Article
by Capper, David and Jones, David T. W and Sill, Martin and Hovestadt, Volker and Schrimpf, Daniel and Sturm, Dominik and Koelsche, Christian and Sahm, Felix and Chavez, Lukas and Reuss, David E and Kratz, Annekathrin and Wefers, Annika K and Huang, Kristin and Pajtler, Kristian W and Schweizer, Leonille and Stichel, Damian and Olar, Adriana and Engel, Nils W and Lindenberg, Kerstin and Harter, Patrick N and Braczynski, Anne K and Plate, Karl H and Dohmen, Hildegard and Garvalov, Boyan K and Coras, Roland and Hölsken, Annett and Hewer, Ekkehard and Bewerunge-Hudler, Melanie and Schick, Matthias and Fischer, Roger and Beschorner, Rudi and Schittenhelm, Jens and Staszewski, Ori and Wani, Khalida and Varlet, Pascale and Pages, Melanie and Temming, Petra and Lohmann, Dietmar and Selt, Florian and Witt, Hendrik and Milde, Till and Witt, Olaf and Aronica, Eleonora and Giangaspero, Felice and Rushing, Elisabeth and Scheurlen, Wolfram and Geisenberger, Christoph and Rodriguez, Fausto J and Becker, Albert and Preusser, Matthias and Haberler, Christine and Bjerkvig, Rolf and Cryan, Jane and Farrell, Michael and Deckert, Martina and Hench, Jürgen and Frank, Stephan and Serrano, Jonathan and Kannan, Kasthuri and Tsirigos, Aristotelis and Brück, Wolfgang and Hofer, Silvia and Brehmer, Stefanie and Seiz-Rosenhagen, Marcel and Hänggi, Daniel and Hans, Volkmar and Rozsnoki, Stephanie and Hansford, Jordan R and Kohlhof, Patricia and Kristensen, Bjarne W and Lechner, Matt and Lopes, Beatriz and Mawrin, Christian and Ketter, Ralf and Kulozik, Andreas and Khatib, Ziad and Heppner, Frank and Koch, Arend and Jouvet, Anne and Keohane, Catherine and Mühleisen, Helmut and Mueller, Wolf and Pohl, Ute and Prinz, Marco and Benner, Axel and Zapatka, Marc and Gottardo, Nicholas G and Driever, Pablo Hernáiz and Kramm, Christof M and Müller, Hermann L and Rutkowski, Stefan and Von Hoff, Katja and Frühwald, Michael C and Gnekow, Astrid and Fleischhack, Gudrun and Tippelt, Stephan and Calaminus, Gabriele and Monoranu, Camelia-Maria and Perry, Arie and Jones, Chris and ...
Nature, ISSN 0028-0836, 03/2018, Volume 555, Issue 7697, pp. 469 - 474
Journal Article
Kongzhi yu Juece/Control and Decision, ISSN 1001-0920, 2014, Volume 29, Issue 5, pp. 815 - 820
Journal Article
Neurocomputing, ISSN 0925-2312, 2011, Volume 74, Issue 16, pp. 2614 - 2623
We consider the problem of classification in environments where training and test data may come from different probability distributions. When the fundamental... 
Imprecise data distribution | Concept drift | Neural networks | Changing operational conditions | Imprecise class distribution | Supervised classification | Posterior probability estimation | PERFORMANCE | CLASS DISTRIBUTIONS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ILLUSION | PROGRESS | TECHNOLOGY | Analysis | Algorithms
Journal Article
Journal Article
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743, 2015, Volume 9426, pp. 160 - 167
Conference Proceeding