WITHDRAWN: I2VM: Incremental import vector machines☆☆☆
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[1] Johan A. K. Suykens,et al. Multi-class kernel logistic regression: a fixed-size implementation , 2007, IJCNN.
[2] Gert Cauwenberghs,et al. Incremental and Decremental Support Vector Machine Learning , 2000, NIPS.
[3] Zhihua Zhang,et al. Bayesian Generalized Kernel Mixed Models , 2011, J. Mach. Learn. Res..
[4] Horst Bischof,et al. Why to Combine Reconstructive and Discriminative Information for Incremental Subspace Learning , 2006 .
[5] Horst Bischof,et al. Semi-supervised On-Line Boosting for Robust Tracking , 2008, ECCV.
[6] Horst Bischof,et al. On-line Boosting and Vision , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[7] Josef Kittler,et al. Incremental Linear Discriminant Analysis Using Sufficient Spanning Set Approximations , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[8] Alexander J. Smola,et al. Online learning with kernels , 2001, IEEE Transactions on Signal Processing.
[9] Paul A. Viola,et al. Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.
[10] Thorsten Joachims,et al. Detecting Concept Drift with Support Vector Machines , 2000, ICML.
[11] Lothar Hotz,et al. The Role of Sequences for Incremental Learning , 2018, ICAART.
[12] Jun Zheng,et al. An Online Incremental Learning Support Vector Machine for Large-scale Data , 2010, ICANN.
[13] Masataka Goto,et al. An Efficient Hybrid Music Recommender System Using an Incrementally Trainable Probabilistic Generative Model , 2008, IEEE Transactions on Audio, Speech, and Language Processing.
[14] S. J. Moss,et al. Performance of the NASA Laser Ranging System in Satellite Tracking , 1971 .
[15] Christopher Joseph Pal,et al. Multi-Conditional Learning: Generative/Discriminative Training for Clustering and Classification , 2006, AAAI.
[16] Ribana Roscher,et al. Incremental Import Vector Machines for Classifying Hyperspectral Data , 2012, IEEE Transactions on Geoscience and Remote Sensing.
[17] Josep Roure Alcobé,et al. A Buffering Strategy to Avoid Ordering Effects in Clustering , 1998, ECML.
[18] Frédéric Achard,et al. Pre-processing of a sample of multi-scene and multi-date Landsat imagery used to monitor forest cover changes over the tropics , 2011 .
[19] Ralf Klinkenberg,et al. Boosting classifiers for drifting concepts , 2007, Intell. Data Anal..
[20] Lawrence Carin,et al. Sparse multinomial logistic regression: fast algorithms and generalization bounds , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[21] Pietro Perona,et al. Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.
[22] Jacob Cohen. A Coefficient of Agreement for Nominal Scales , 1960 .
[23] Yoav Freund,et al. Experiments with a New Boosting Algorithm , 1996, ICML.
[24] G. Cawley,et al. Efficient model selection for kernel logistic regression , 2004, ICPR 2004.
[25] Tin Kam Ho,et al. The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[26] Horst Bischof,et al. On-line Random Forests , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.
[27] Fred Glover,et al. Tabu Search - Part II , 1989, INFORMS J. Comput..
[28] Marc G. Genton,et al. Classes of Kernels for Machine Learning: A Statistics Perspective , 2002, J. Mach. Learn. Res..
[29] Tom Minka,et al. Principled Hybrids of Generative and Discriminative Models , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[30] Leo Breiman,et al. Classification and Regression Trees , 1984 .
[31] Rajat Raina,et al. Classification with Hybrid Generative/Discriminative Models , 2003, NIPS.
[32] Ichiro Takeuchi,et al. Multiple Incremental Decremental Learning of Support Vector Machines , 2009, IEEE Transactions on Neural Networks.
[33] Michael E. Tipping. Sparse Bayesian Learning and the Relevance Vector Machine , 2001, J. Mach. Learn. Res..
[34] Shaoning Pang,et al. Incremental linear discriminant analysis for classification of data streams , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[35] Tat-Jun Chin,et al. Incremental Kernel Principal Component Analysis , 2007, IEEE Transactions on Image Processing.
[36] Silvia Scarpetta,et al. Uncertainty Analysis for the Classification of Multispectral Satellite Images Using SVMs and SOMs , 2010, IEEE Transactions on Geoscience and Remote Sensing.
[37] Martial Hebert,et al. Discriminative Random Fields , 2006, International Journal of Computer Vision.
[38] Volker Roth,et al. Probabilistic Discriminative Kernel Classifiers for Multi-class Problems , 2001, DAGM-Symposium.
[39] Uwe Weidner,et al. Support vector machines, import vector machines and relevance vector machines for hyperspectral classification — A comparison , 2011, 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).
[40] J. Friedman. Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .
[41] Qi Zhao,et al. Co-Tracking Using Semi-Supervised Support Vector Machines , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[42] Bianca Zadrozny,et al. Learning and making decisions when costs and probabilities are both unknown , 2001, KDD '01.
[43] Jason Weston,et al. Solving multiclass support vector machines with LaRank , 2007, ICML '07.
[44] Pat Langley,et al. Constraints on Tree Structure in Concept Formation , 1991, IJCAI.
[45] Horst Bischof,et al. Incremental LDA Learning by Combining Reconstructive and Discriminative Approaches , 2007, BMVC.
[46] Glenn Fung,et al. Incremental Support Vector Machine Classification , 2002, SDM.
[47] Ji Zhu,et al. Kernel Logistic Regression and the Import Vector Machine , 2001, NIPS.
[48] Antoine Cornuéjols,et al. Getting Order Independence in Incremental Learning , 1993, ECML.
[49] Rich Caruana,et al. Predicting good probabilities with supervised learning , 2005, ICML.
[50] Jason Weston,et al. Curriculum learning , 2009, ICML '09.
[51] Peter Sollich,et al. Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities , 2002, Machine Learning.
[52] John A. Richards,et al. Analysis of remotely sensed data: the formative decades and the future , 2005, IEEE Transactions on Geoscience and Remote Sensing.
[53] J. Cihlar. Land cover mapping of large areas from satellites: Status and research priorities , 2000 .
[54] Samy Bengio,et al. A Probabilistic Interpretation of SVMs with an Application to Unbalanced Classification , 2005, NIPS.
[55] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[56] Bernt Schiele,et al. Integrating representative and discriminant models for object category detection , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[57] Michael A. Wulder,et al. Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas , 2002 .
[58] S. Sathiya Keerthi,et al. A Fast Dual Algorithm for Kernel Logistic Regression , 2002, 2007 International Joint Conference on Neural Networks.
[59] Nello Cristianini,et al. Large Margin DAGs for Multiclass Classification , 1999, NIPS.
[60] Stefan Rüping,et al. Incremental Learning with Support Vector Machines , 2001, ICDM.
[61] Trevor Hastie,et al. The Elements of Statistical Learning , 2001 .
[62] Ribana Roscher,et al. Incremental import vector machines for large area land cover classification , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).
[63] D. M. Titterington,et al. On the generative-discriminative tradeoff approach: Interpretation, asymptotic efficiency and classification performance , 2010, Comput. Stat. Data Anal..
[64] Gavin C. Cawley,et al. Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation , 2006, NIPS.
[65] Bernhard Schölkopf,et al. Support Vector Machines as Probabilistic Models , 2011, ICML.
[66] P. Strobl,et al. Pan-European Forest/Non-Forest Mapping with Landsat ETM+ and CORINE Land Cover 2000 Data , 2009 .
[67] Carlo Tomasi,et al. Efficient Visual Object Tracking with Online Nearest Neighbor Classifier , 2010, ACCV.
[68] Georg Heigold,et al. Latent Log-Linear Models for Handwritten Digit Classification , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[69] Rich Caruana,et al. An empirical comparison of supervised learning algorithms , 2006, ICML.
[70] Antonio J. Plaza,et al. Hyperspectral Image Segmentation Using a New Bayesian Approach With Active Learning , 2011, IEEE Transactions on Geoscience and Remote Sensing.
[71] Pedro M. Domingos,et al. Tree Induction for Probability-Based Ranking , 2003, Machine Learning.
[72] Josef Kellndorfer,et al. Large-Area Classification and Mapping of Forest and Land Cover in the Brazilian Amazon: A Comparative Analysis of ALOS/PALSAR and Landsat Data Sources , 2010, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[73] Patrick Hostert,et al. Land cover mapping of large areas using chain classification of neighboring Landsat satellite images , 2009 .
[74] Ales Leonardis,et al. Online Discriminative Kernel Density Estimation , 2010, 2010 20th International Conference on Pattern Recognition.
[75] Zhuowen Tu,et al. Combining Generative and Discriminative Models for Semantic Segmentation of CT Scans via Active Learning , 2011, IPMI.
[76] Zhuowen Tu,et al. Learning Generative Models via Discriminative Approaches , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[77] Bill Triggs,et al. Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[78] Hans Spada,et al. Learning in Humans and Machines , 1995 .
[79] William J. Emery,et al. Active Learning Methods for Remote Sensing Image Classification , 2009, IEEE Transactions on Geoscience and Remote Sensing.
[80] Leo Breiman,et al. Random Forests , 2001, Machine Learning.