Incremental Learning of Concept Drift in Nonstationary Environments
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[1] Philip S. Yu,et al. Mining concept-drifting data streams using ensemble classifiers , 2003, KDD '03.
[2] Abraham Bernstein,et al. Entropy-based Concept Shift Detection , 2006, Sixth International Conference on Data Mining (ICDM'06).
[3] Kyosuke Nishida,et al. Adaptive Classifiers-Ensemble System for Tracking Concept Drift , 2007, 2007 International Conference on Machine Learning and Cybernetics.
[4] Haixun Wang,et al. A Low-Granularity Classifier for Data Streams with Concept Drifts and Biased Class Distribution , 2007, IEEE Transactions on Knowledge and Data Engineering.
[5] Ludmila I. Kuncheva,et al. Classifier Ensembles for Changing Environments , 2004, Multiple Classifier Systems.
[6] David B. Skillicorn,et al. Classification Using Streaming Random Forests , 2011, IEEE Transactions on Knowledge and Data Engineering.
[7] F. Bartlett,et al. Remembering: A Study in Experimental and Social Psychology , 1932 .
[8] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.
[9] Abraham Kandel,et al. Real-time data mining of non-stationary data streams from sensor networks , 2008, Inf. Fusion.
[10] Abraham Kandel,et al. Info-fuzzy algorithms for mining dynamic data streams , 2008, Appl. Soft Comput..
[11] Ralf Klinkenberg,et al. Learning drifting concepts: Example selection vs. example weighting , 2004, Intell. Data Anal..
[12] Roman Garnett,et al. Sequential non-stationary dynamic classification with sparse feedback , 2010, Pattern Recognit..
[13] S. Hoeglinger,et al. Use of Hoeffding trees in concept based data stream mining , 2007, 2007 Third International Conference on Information and Automation for Sustainability.
[14] Ralf Klinkenberg,et al. Boosting classifiers for drifting concepts , 2007, Intell. Data Anal..
[15] Rafael Morales Bueno,et al. Learning in Environments with Unknown Dynamics: Towards more Robust Concept Learners , 2007, J. Mach. Learn. Res..
[16] Haibo He,et al. Towards incremental learning of nonstationary imbalanced data stream: a multiple selectively recursive approach , 2011, Evol. Syst..
[17] Xindong Wu,et al. Dynamic classifier selection for effective mining from noisy data streams , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).
[18] Geoff Hulten,et al. Mining time-changing data streams , 2001, KDD '01.
[19] João Gama,et al. Learning with Drift Detection , 2004, SBIA.
[20] Xin Yao,et al. The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift , 2010, IEEE Transactions on Knowledge and Data Engineering.
[21] Geoff Holmes,et al. Accurate Ensembles for Data Streams: Combining Restricted Hoeffding Trees using Stacking , 2010, ACML.
[22] Cesare Alippi,et al. Just-in-Time Adaptive Classifiers—Part II: Designing the Classifier , 2008, IEEE Transactions on Neural Networks.
[23] Ludmila I. Kuncheva,et al. Classifier Ensembles for Detecting Concept Change in Streaming Data: Overview and Perspectives , 2008 .
[24] Gavin Brown,et al. Learn++.MF: A random subspace approach for the missing feature problem , 2010, Pattern Recognit..
[25] L. S. Vygotskiĭ,et al. Mind in society : the development of higher psychological processes , 1978 .
[26] Philip S. Yu,et al. A General Framework for Mining Concept-Drifting Data Streams with Skewed Distributions , 2007, SDM.
[27] W. Bastiaan Kleijn,et al. Codebook-Based Bayesian Speech Enhancement for Nonstationary Environments , 2007, IEEE Transactions on Audio, Speech, and Language Processing.
[28] J. Piaget. Six Psychological Studies , 1967 .
[29] Leszek Rutkowski,et al. Adaptive probabilistic neural networks for pattern classification in time-varying environment , 2004, IEEE Transactions on Neural Networks.
[30] Cesare Alippi,et al. Just-in-Time Adaptive Classifiers—Part I: Detecting Nonstationary Changes , 2008, IEEE Transactions on Neural Networks.
[31] Robi Polikar,et al. Learning concept drift in nonstationary environments using an ensemble of classifiers based approach , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).
[32] Mykola Pechenizkiy,et al. Handling Local Concept Drift with Dynamic Integration of Classifiers: Domain of Antibiotic Resistance in Nosocomial Infections , 2006, 19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06).
[33] M. Appel,et al. Equilibration : theory, research, and application , 1977 .
[34] Mykola Pechenizkiy,et al. Dynamic integration of classifiers for handling concept drift , 2008, Inf. Fusion.
[35] Robi Polikar,et al. Incremental learning in non-stationary environments with concept drift using a multiple classifier based approach , 2008, 2008 19th International Conference on Pattern Recognition.
[36] Robi Polikar,et al. Incremental Learning of Variable Rate Concept Drift , 2009, MCS.
[37] J. Flavell. Piaget's Legacy , 1996 .
[38] R. Schapire. The Strength of Weak Learnability , 1990, Machine Learning.
[39] Avrim Blum,et al. Empirical Support for Winnow and Weighted-Majority Algorithms: Results on a Calendar Scheduling Domain , 2004, Machine Learning.
[40] Niall M. Adams,et al. lambda-Perceptron: An adaptive classifier for data streams , 2011, Pattern Recognit..
[41] Marcus A. Maloof,et al. Dynamic weighted majority: a new ensemble method for tracking concept drift , 2003, Third IEEE International Conference on Data Mining.
[42] Haibo He,et al. IMORL: Incremental Multiple-Object Recognition and Localization , 2008, IEEE Transactions on Neural Networks.
[43] William Nick Street,et al. A streaming ensemble algorithm (SEA) for large-scale classification , 2001, KDD '01.
[44] Vasant Honavar,et al. Learn++: an incremental learning algorithm for supervised neural networks , 2001, IEEE Trans. Syst. Man Cybern. Part C.
[45] Robi Polikar,et al. Learn$^{++}$ .NC: Combining Ensemble of Classifiers With Dynamically Weighted Consult-and-Vote for Efficient Incremental Learning of New Classes , 2009, IEEE Transactions on Neural Networks.
[46] Nikunj C. Oza,et al. Online Ensemble Learning , 2000, AAAI/IAAI.
[47] E. A. de Oliveira. The Rosenblatt Bayesian Algorithm Learning in a Nonstationary Environment , 2007, IEEE Transactions on Neural Networks.
[48] Sameer Singh,et al. Novelty detection: a review - part 2: : neural network based approaches , 2003, Signal Process..
[49] Robi Polikar,et al. Incremental learning in nonstationary environments with controlled forgetting , 2009, 2009 International Joint Conference on Neural Networks.
[50] Neil D. Lawrence,et al. Dataset Shift in Machine Learning , 2009 .
[51] Marcus A. Maloof,et al. Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts , 2007, J. Mach. Learn. Res..
[52] Jiawei Han,et al. On Appropriate Assumptions to Mine Data Streams: Analysis and Practice , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).
[53] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.
[54] J. C. Schlimmer,et al. Incremental learning from noisy data , 2004, Machine Learning.
[55] Albert Bifet,et al. Adaptive learning and mining for data streams and frequent patterns , 2009, SKDD.
[56] L. Vygotsky. Mind in Society: The Development of Higher Psychological Processes: Harvard University Press , 1978 .
[57] Stephen Grossberg,et al. Nonlinear neural networks: Principles, mechanisms, and architectures , 1988, Neural Networks.
[58] Brian J. Reiser,et al. Scaffolding Complex Learning: The Mechanisms of Structuring and Problematizing Student Work , 2004, The Journal of the Learning Sciences.
[59] Wei-Pang Yang,et al. Mining decision rules on data streams in the presence of concept drifts , 2009, Expert Syst. Appl..
[60] Philip S. Yu,et al. Classifying Data Streams with Skewed Class Distributions and Concept Drifts , 2008, IEEE Internet Computing.
[61] N. Littlestone. Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm , 1987, 28th Annual Symposium on Foundations of Computer Science (sfcs 1987).
[62] Gerhard Widmer,et al. Learning in the Presence of Concept Drift and Hidden Contexts , 1996, Machine Learning.