Stochastic functional descent for learning Support Vector Machines

[1]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[2]  G. Wahba,et al.  A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines , 1970 .

[3]  J. Friedman Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .

[4]  Quoc V. Le,et al.  Proximal regularization for online and batch learning , 2009, ICML '09.

[5]  Jason Weston,et al.  Fast Kernel Classifiers with Online and Active Learning , 2005, J. Mach. Learn. Res..

[6]  J. Platt Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines , 1998 .

[7]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[8]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[9]  Ohad Shamir,et al.  Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization , 2011, ICML.

[10]  Yoram Singer,et al.  Pegasos: primal estimated sub-gradient solver for SVM , 2011, Math. Program..

[11]  Andrew Zisserman,et al.  Multiple kernels for object detection , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[12]  Yoram Singer,et al.  Efficient Online and Batch Learning Using Forward Backward Splitting , 2009, J. Mach. Learn. Res..

[13]  Nathan Srebro,et al.  The Kernelized Stochastic Batch Perceptron , 2012, ICML.

[14]  Thomas Hofmann,et al.  Large Margin Methods for Structured and Interdependent Output Variables , 2005, J. Mach. Learn. Res..

[15]  Daphne Koller,et al.  Multiclass Boosting with Hinge Loss based on Output Coding , 2011, ICML.

[16]  Mark W. Schmidt,et al.  Block-Coordinate Frank-Wolfe Optimization for Structural SVMs , 2012, ICML.

[17]  S. V. N. Vishwanathan,et al.  A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning , 2008, J. Mach. Learn. Res..

[18]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[19]  Alex Smola,et al.  Kernel methods in machine learning , 2007, math/0701907.

[20]  Christoph H. Lampert,et al.  Learning to Localize Objects with Structured Output Regression , 2008, ECCV.

[21]  H. Brendan McMahan,et al.  A Unified View of Regularized Dual Averaging and Mirror Descent with Implicit Updates , 2010, 1009.3240.

[22]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Thorsten Joachims,et al.  Training structural svms with kernels using sampled cuts , 2008, KDD.

[24]  Thorsten Joachims,et al.  Cutting-plane training of structural SVMs , 2009, Machine Learning.

[25]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[26]  Ambuj Tewari,et al.  Composite objective mirror descent , 2010, COLT 2010.

[27]  Lin Xiao,et al.  Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization , 2009, J. Mach. Learn. Res..

[28]  Koby Crammer,et al.  On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines , 2002, J. Mach. Learn. Res..

[29]  Stephen P. Boyd,et al.  Proximal Algorithms , 2013, Found. Trends Optim..

[30]  Stan Sclaroff,et al.  Fast globally optimal 2D human detection with loopy graph models , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[31]  J. Andrew Bagnell,et al.  Maximum margin planning , 2006, ICML.

[32]  Charless C. Fowlkes,et al.  Discriminative Models for Multi-Class Object Layout , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[33]  Pietro Perona,et al.  Object class recognition by unsupervised scale-invariant learning , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[34]  Peter L. Bartlett,et al.  Implicit Online Learning , 2010, ICML.

[35]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[36]  Alexander J. Smola,et al.  Bundle Methods for Machine Learning , 2007, NIPS.

[37]  J. Friedman Greedy function approximation: A gradient boosting machine. , 2001 .