Superfast-Trainable Multi-Class Probabilistic Classifier by Least-Squares Posterior Fitting
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[1] Chih-Jen Lin,et al. Probability Estimates for Multi-class Classification by Pairwise Coupling , 2003, J. Mach. Learn. Res..
[2] Thorsten Joachims,et al. Making large scale SVM learning practical , 1998 .
[3] Samy Bengio,et al. SVMTorch: Support Vector Machines for Large-Scale Regression Problems , 2001, J. Mach. Learn. Res..
[4] E. Newport,et al. Science Current Directions in Psychological Statistical Learning : from Acquiring Specific Items to Forming General Rules on Behalf Of: Association for Psychological Science , 2022 .
[5] Sören Sonnenburg,et al. Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization , 2009, J. Mach. Learn. Res..
[6] Masashi Sugiyama,et al. Conic Programming for Multitask Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[7] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[8] Jing Peng,et al. SVM vs regularized least squares classification , 2004, ICPR 2004.
[9] Takafumi Kanamori,et al. Theoretical Analysis of Density Ratio Estimation , 2010, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..
[10] P. Bartlett,et al. Probabilities for SV Machines , 2000 .
[11] R. Tibshirani,et al. Least angle regression , 2004, math/0406456.
[12] Michael I. Jordan,et al. On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes , 2001, NIPS.
[13] I. Song,et al. Working Set Selection Using Second Order Information for Training Svm, " Complexity-reduced Scheme for Feature Extraction with Linear Discriminant Analysis , 2022 .
[14] Tom Heskes,et al. Task Clustering and Gating for Bayesian Multitask Learning , 2003, J. Mach. Learn. Res..
[15] Ivor W. Tsang,et al. Core Vector Machines: Fast SVM Training on Very Large Data Sets , 2005, J. Mach. Learn. Res..
[16] Hao Helen Zhang,et al. Multiclass Proximal Support Vector Machines , 2006 .
[17] Shun-ichi Amari,et al. A Theory of Adaptive Pattern Classifiers , 1967, IEEE Trans. Electron. Comput..
[18] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[19] John C. Platt,et al. Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .
[20] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[21] Robert Tibshirani,et al. The Entire Regularization Path for the Support Vector Machine , 2004, J. Mach. Learn. Res..
[22] Johan A. K. Suykens,et al. Least Squares Support Vector Machines , 2002 .
[23] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[24] T. Minka. A comparison of numerical optimizers for logistic regression , 2004 .
[25] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[26] Masashi Sugiyama,et al. Condition Number Analysis of Kernel-based Density Ratio Estimation , 2009, 0912.2800.
[27] Alexander J. Smola,et al. A scalable modular convex solver for regularized risk minimization , 2007, KDD '07.
[28] Chih-Jen Lin,et al. LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..
[29] Alexander J. Smola,et al. Learning with kernels , 1998 .
[30] Glenn Fung,et al. Multicategory Proximal Support Vector Machine Classifiers , 2005, Machine Learning.
[31] Rich Caruana,et al. Multitask Learning , 1998, Encyclopedia of Machine Learning and Data Mining.
[32] T. Poggio,et al. Regularized Least-Squares Classification 133 In practice , although , 2007 .
[33] Stephen P. Boyd,et al. An Interior-Point Method for Large-Scale l1-Regularized Logistic Regression , 2007, J. Mach. Learn. Res..
[34] Masashi Sugiyama,et al. Improving the Accuracy of Least-Squares Probabilistic Classifiers , 2011, IEICE Trans. Inf. Syst..
[35] Thorsten Joachims,et al. Training linear SVMs in linear time , 2006, KDD '06.
[36] Takafumi Kanamori,et al. A Least-squares Approach to Direct Importance Estimation , 2009, J. Mach. Learn. Res..