An asymmetric stagewise least square loss function for imbalanced classification
暂无分享,去创建一个
[1] Nello Cristianini,et al. Controlling the Sensitivity of Support Vector Machines , 1999 .
[2] Stephen Kwek,et al. Applying Support Vector Machines to Imbalanced Datasets , 2004, ECML.
[3] Peng Li,et al. Hybrid Kernel Machine Ensemble for Imbalanced Data Sets , 2006, 18th International Conference on Pattern Recognition (ICPR'06).
[4] Olvi L. Mangasarian,et al. Multisurface proximal support vector machine classification via generalized eigenvalues , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[5] Johan A. K. Suykens,et al. Benchmarking Least Squares Support Vector Machine Classifiers , 2004, Machine Learning.
[6] Adam Kowalczyk,et al. Extreme re-balancing for SVMs: a case study , 2004, SKDD.
[7] José Carlos Príncipe,et al. The C-loss function for pattern classification , 2014, Pattern Recognit..
[8] Olvi L. Mangasarian,et al. Generalized Support Vector Machines , 1998 .
[9] Michael I. Jordan,et al. Convexity, Classification, and Risk Bounds , 2006 .
[10] Haibo He,et al. Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.
[11] Pedro M. Domingos. MetaCost: a general method for making classifiers cost-sensitive , 1999, KDD '99.
[12] Yuh-Jye Lee,et al. RSVM: Reduced Support Vector Machines , 2001, SDM.
[13] Stan Matwin,et al. Addressing the Curse of Imbalanced Training Sets: One-Sided Selection , 1997, ICML.
[14] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[15] Joarder Kamruzzaman,et al. z-SVM: An SVM for Improved Classification of Imbalanced Data , 2006, Australian Conference on Artificial Intelligence.
[16] Nuno Vasconcelos,et al. On the design of robust classifiers for computer vision , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[17] C. Scott. Calibrated asymmetric surrogate losses , 2012 .
[18] Ming-Syan Chen,et al. On Generalizable Low False-Positive Learning Using Asymmetric Support Vector Machines , 2013, IEEE Transactions on Knowledge and Data Engineering.
[19] Jianjun Wang,et al. Margin calibration in SVM class-imbalanced learning , 2009, Neurocomputing.
[20] Yi-Hung Liu,et al. Face Recognition Using Total Margin-Based Adaptive Fuzzy Support Vector Machines , 2007, IEEE Transactions on Neural Networks.
[21] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[22] C. Lee Giles,et al. Learning on the border: active learning in imbalanced data classification , 2007, CIKM '07.
[23] John Shawe-Taylor,et al. Refining Kernels for Regression and Uneven Classification Problems , 2003, AISTATS.
[24] Tong Zhang. Statistical behavior and consistency of classification methods based on convex risk minimization , 2003 .
[25] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[26] Yanqing Zhang,et al. SVMs Modeling for Highly Imbalanced Classification , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[27] Tom Fawcett,et al. ROC Graphs: Notes and Practical Considerations for Researchers , 2007 .
[28] Glenn Fung,et al. Multicategory Proximal Support Vector Machine Classifiers , 2005, Machine Learning.
[29] Vasile Palade,et al. FSVM-CIL: Fuzzy Support Vector Machines for Class Imbalance Learning , 2010, IEEE Transactions on Fuzzy Systems.
[30] Peter Williams,et al. A Geometrical Method to Improve Performance of the Support Vector Machine , 2007, IEEE Transactions on Neural Networks.
[31] Jason Weston,et al. Trading convexity for scalability , 2006, ICML.
[32] Bao-Gang Hu,et al. A New Strategy of Cost-Free Learning in the Class Imbalance Problem , 2014, IEEE Transactions on Knowledge and Data Engineering.
[33] Edward Y. Chang,et al. KBA: kernel boundary alignment considering imbalanced data distribution , 2005, IEEE Transactions on Knowledge and Data Engineering.
[34] Shuang-Hong Yang,et al. A Stagewise Least Square Loss Function for Classification , 2008, SDM.
[35] Charles Elkan,et al. The Foundations of Cost-Sensitive Learning , 2001, IJCAI.
[36] Andy J. Keane,et al. Some Greedy Learning Algorithms for Sparse Regression and Classification with Mercer Kernels , 2003, J. Mach. Learn. Res..
[37] W. Wong,et al. On ψ-Learning , 2003 .
[38] Bao-Gang Hu,et al. Cost-Free Learning for Support Vector Machines with a Reject Option , 2013, 2013 IEEE 13th International Conference on Data Mining Workshops.
[39] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[40] Yunqian Ma,et al. Foundations of Imbalanced Learning , 2013 .
[41] Yunqian Ma,et al. Imbalanced Learning: Foundations, Algorithms, and Applications , 2013 .
[42] Yoshua. Bengio,et al. Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..