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keywords = perceptron (66 entries)  Select: All None   Action: Show BibTeX

Stephane Canu. 2014. Understanding SVM, February. Lectures given at the Institute of Mathematics and Statistics, University of Sao Paulo. (slides and code). [perceptron] url google scholar
Manuel Fernández-Delgado , Eva Cernadas , Senén Barro , Jorge Ribeiro and José Neves . 2014. Direct Kernel Perceptron (DKP): Ultra-fast kernel ELM-based classification with non-iterative closed-form weight calculation. Neural Networks, vol 50, no 0, pp 60 - 71. cit 2. [perceptron] url google scholar
Shai Shalev-Shwartz and S. Ben-David. 2014. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press. [book.ml, ebook, perceptron] url pdf google scholar books
Kai Zhao and Liang Huang. 2013. Minibatch and Parallelization for Online Large Margin Structured Learning. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 370--379, Atlanta, Georgia, June. Association for Computational Linguistics. [perceptron] url google scholar
Steven C. H. Hoi. 2013. Online Learning for Big Data Mining. Tutorial at SDM2013. (presentation). [perceptron] pdf google scholar
Andrew Cotter, Shai Shalev-Shwartz and Nati Srebro. 2013. Learning optimally sparse support vector machines. In Proceedings of the 30th International Conference on Machine Learning (ICML-13), pp 266--274. cit 3. [perceptron] pdf pdf google scholar
Simone Filice. 2013. Online machine learning. University of Roma Tor Vergata. (presentation). [perceptron] pdf google scholar
Hal Daume III. 2012. The Perceptron. In A Course in Machine Learning. ciml.info. (book chapter). [perceptron] pdf annote google scholar
Y.S. Abu-Mostafa, M. Magdon-Ismail and H.T. Lin. 2012. Learning from Data: A Short Course. AMLBook.com. [book.ml, perceptron] url url pdf google scholar books
Steven C.H. Hoi, Jialei Wang and Peilin Zhao. 2012. LIBOL: A Library for Online Learning Algorithms. Nanyang Technological University. (software). [perceptron] url pdf pdf google scholar
Brian Roark. 2012. Perceptrons. Slides for CSE506-TNL. (presentation). [perceptron] pdf google scholar
M. Mohri, A. Rostamizadeh and A. Talwalkar. 2012. Foundations of Machine Learning. MIT Press. [book.ml, ebook, perceptron] url pdf google scholar books
Peilin Zhao, Jialei Wang, Pengcheng Wu, Rong Jin and Steven CH Hoi. 2012. Fast bounded online gradient descent algorithms for scalable kernel-based online learning. In ICML. cit 9. [perceptron] pdf google scholar
Giovanni Cavallanti, Nicolò Cesa-Bianchi and Claudio Gentile. 2011. Learning noisy linear classifiers via adaptive and selective sampling. Machine learning, vol 83, no 1, pp 71--102. Springer. (SSMD,SS). [perceptron] pdf google scholar
Francesco Orabona and Nicolo Cesa-Bianchi. 2011. Better algorithms for selective sampling. In Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp 433--440. (BBQ,DGS_MOD). [perceptron] pdf google scholar
Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro and Andrew Cotter. 2011. Pegasos: Primal estimated sub-gradient solver for svm. Mathematical programming, vol 127, no 1, pp 3--30. Springer. (PEGASOS). [perceptron] pdf google scholar
Shai Shalev-Shwartz. 2011. Online learning and online convex optimization. Foundations and Trends in Machine Learning, vol 4, no 2, pp 107--194. [perceptron] pdf google scholar
Roberto Paredes. 2010. Online Learning, July. PASCAL Bootcamp in Machine Learning, Marseille. (video lecture). [perceptron] url google scholar
Francesco Orabona, Claudio Castellini, Barbara Caputo, Luo Jie and Giulio Sandini. 2010. On-line independent support vector machines. Pattern Recognition, vol 43, no 4, pp 1402--1412. Elsevier. (OISVM). [perceptron] pdf google scholar
M. Elad. 2010. Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing. Springer. [book.ml, perceptron, ebook] url pdf google scholar books
Francesco Orabona, Joseph Keshet and Barbara Caputo. 2009. Bounded kernel-based online learning. The Journal of Machine Learning Research, vol 10, pp 2643--2666. JMLR. org. (PROJECTRON). [perceptron] url google scholar
Shai Shalev-Shwartz. 2009. Introduction to Machine Learning. (lecture notes). [perceptron] pdf url google scholar
Francesco Orabona . 2009. DOGMA: a MATLAB toolbox for Online Learning. Software available at \urlhttp://dogma.sourceforge.net. [perceptron] url google scholar
Thorsten Joachims. 2008. Structured Output Prediction with Structural SVMs, July. PASCAL 6th International Workshop on Mining and Learning with Graphs (MLG), Helsinki. (video lecture). [perceptron] url google scholar
Ofer Dekel, Shai Shalev-Shwartz and Yoram Singer. 2008. The Forgetron: A kernel-based perceptron on a budget. SIAM Journal on Computing, vol 37, no 5, pp 1342--1372. SIAM. (FORGETRON). cit 85. [perceptron] pdf google scholar
Francesco Orabona, Joseph Keshet and Barbara Caputo. 2008. The projectron: a bounded kernel-based perceptron. In Proceedings of the 25th international conference on Machine learning, pp 720--727. ACM. (PROJECTRON). cit 48. [perceptron] pdf annote google scholar
Shai Shalev-Shwartz and Nathan Srebro. 2008. SVM optimization: inverse dependence on training set size. In Proceedings of the 25th international conference on Machine learning, pp 928--935. ACM. [perceptron] pdf google scholar
Thorsten Joachims. 2008. SVM-Struct: Support Vector Machine for Complex Outputs. (software). [perceptron] url google scholar
Giovanni Cavallanti, Nicolò Cesa-Bianchi and Claudio Gentile. 2007. Tracking the best hyperplane with a simple budget perceptron. Machine Learning, vol 69, no 2-3, pp 143--167. Springer. (RBP). [perceptron] pdf annote google scholar
Daniel Garcia, Ana Gonzales and Jose R. Dorronsoro. 2007. Coefficient Structure of Kernel Perceptrons and Support Vector Reduction. In IWINAC, Part I, LNCS 4527. cit 0. [perceptron] pdf annote google scholar
Shai Shalev-Shwartz and Yoram Singer. 2007. A primal-dual perspective of online learning algorithms. Machine Learning, vol 69, no 2-3, pp 115--142. Springer. [perceptron] pdf google scholar
Natasha Singh-Miller. 2007. Trigger-based language modeling using a loss-sensitive perceptron algorithm. In IEEE ICASSP. Citeseer. cit 30. [perceptron] pdf google scholar
Shai Shalev-Shwartz. 2007. Online Learning: Theory, Algorithms, and Applications. Phd Thesis. Hebrew University. [perceptron] pdf google scholar
Daniel Garcia. 2006. Optimal Support Vector Selection for Kernel Perceptrons, October. PASCAL Learning Conference, Vilanova. (video lecture). [perceptron] url google scholar
Li Cheng, SVN Vishwanathan, Dale Schuurmans, Shaojun Wang and Terry Caelli. 2006. Implicit online learning with kernels. In NIPS, pp 249--256. [perceptron] google scholar
Nicolò Cesa-Bianchi, Claudio Gentile, Luca Zaniboni and Manfred Warmuth. 2006. Worst-Case Analysis of Selective Sampling for Linear Classification.. Journal of Machine Learning Research, vol 7, no 7. (sel_perc,sel_ada_perc,sole). [perceptron] pdf google scholar
Koby Crammer, Ofer Dekel, Joseph Keshet, Shai Shalev-Shwartz and Yoram Singer. 2006. Online passive-aggressive algorithms. The Journal of Machine Learning Research, vol 7, pp 551--585. JMLR. org. (PA). [perceptron] pdf annote google scholar
Yann LeCun, Sumit Chopra, Raia Hadsell, M Ranzato and F Huang. 2006. A tutorial on energy-based learning. Predicting structured data. [perceptron] pdf pdf google scholar
Nicolò Cesa-Bianchi. 2005. On-line linear learning algorithms, November. PASCAL: The Analysis of Patterns, Erice. (video lecture). [perceptron] url google scholar
Jason Weston, Antoine Bordes, Léon Bottou, et al. 2005. Online (and offline) on an even tighter budget. In Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics, pp 413--420. cit 55. [perceptron] pdf annote google scholar
Antoine Bordes, Seyda Ertekin, Jason Weston and Léon Bottou. 2005. Fast kernel classifiers with online and active learning. The Journal of Machine Learning Research, vol 6, pp 1579--1619. JMLR. org. (LASVM). cit 345. [perceptron] pdf google scholar
Nicolo Cesa-Bianchi, Alex Conconi and Claudio Gentile. 2005. A second-order perceptron algorithm. SIAM Journal on Computing, vol 34, no 3, pp 640--668. SIAM. (SOP). [perceptron] pdf google scholar
Ofer Dekel, Shai Shalev-Shwartz and Yoram Singer. 2005. The Forgetron: A kernel-based perceptron on a fixed budget. In NIPS. (FORGETRON). cit 68. [perceptron] pdf annote google scholar
Antoine Bordes and Léon Bottou. 2005. The Huller: a simple and efficient online SVM. In Machine Learning: ECML 2005, pp 505--512. Springer. cit 55. [perceptron] google scholar
Michael Collins and Brian Roark. 2004. Incremental parsing with the perceptron algorithm. In Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, pp 111. Association for Computational Linguistics. cit 214. [perceptron] google scholar
Jyrki Kivinen, Alexander J Smola and Robert C Williamson. 2004. Online learning with kernels. Signal Processing, IEEE Transactions on, vol 52, no 8, pp 2165--2176. IEEE. (NORMA). cit 377. [perceptron] pdf annote google scholar
John Shawe-Taylor and Nello Cristianini. 2004. Kernel methods for pattern analysis. Cambridge university press. cit 4268. [perceptron] pdf google scholar books
Brian Roark, Murat Saraclar, Michael Collins and Mark Johnson. 2004. Discriminative language modeling with conditional random fields and the perceptron algorithm. In Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, pp 47. Association for Computational Linguistics. cit 125. [perceptron] google scholar
Koby Crammer and Yoram Singer. 2003. A family of additive online algorithms for category ranking. The Journal of Machine Learning Research, vol 3, pp 1025--1058. JMLR. org. [perceptron] google scholar
Koby Crammer, Jaz S Kandola and Yoram Singer. 2003. Online Classification on a Budget.. In NIPS, vol 2, pp 5. cit 99. [perceptron] pdf annote google scholar
Koby Crammer and Yoram Singer. 2003. Ultraconservative online algorithms for multiclass problems. The Journal of Machine Learning Research, vol 3, pp 951--991. JMLR. org. (MIRA,perceptron_multi). [perceptron] pdf google scholar
Michael Collins. 2002. Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10, pp 1--8. Association for Computational Linguistics. cit 1308. [comp542, perceptron] pdf annote google scholar
Yi Li and Philip M Long. 2002. The relaxed online maximum margin algorithm. Machine Learning, vol 46, no 1-3, pp 361--387. Springer. (ROMMA). cit 157. [perceptron] google scholar
Bernhard Schölkopf and Alexander J Smola. 2002. Learning with kernels: Support vector machines, regularization, optimization, and beyond. MIT press. [perceptron, book.ml, ebook] pdf google scholar books
Tom Downs, Kevin E Gates and Annette Masters. 2002. Exact simplification of support vector solutions. The Journal of Machine Learning Research, vol 2, pp 293--297. JMLR. org. cit 205. [perceptron] google scholar
Pascal Vincent and Yoshua Bengio. 2002. Kernel matching pursuit. Machine Learning, vol 48, no 1-3, pp 165--187. Springer. cit 222. [perceptron] google scholar
Michael Collins. 2002. Ranking algorithms for named-entity extraction: Boosting and the voted perceptron. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp 489--496. Association for Computational Linguistics. cit 231. [perceptron] google scholar
Michael Collins and Nigel Duffy. 2002. New ranking algorithms for parsing and tagging: Kernels over discrete structures, and the voted perceptron. In Proceedings of the 40th annual meeting on association for computational linguistics, pp 263--270. Association for Computational Linguistics. cit 480. [perceptron] google scholar
Koby Crammer and Yoram Singer. 2001. On the algorithmic implementation of multiclass kernel-based vector machines. The Journal of Machine Learning Research, vol 2, pp 265--292. JMLR. org. [perceptron] pdf google scholar
Michael Collins, Nigel Duffy, et al. 2001. Convolution kernels for natural language. In NIPS, vol 2001, pp 625--632. cit 706. [perceptron] google scholar
Claudio Gentile. 2001. A new approximate maximal margin classification algorithm. The Journal of Machine Learning Research, vol 2, pp 213--242. JMLR. org. (ALMA). cit 176. [perceptron] pdf annote google scholar
Michael E Tipping. 2001. Sparse Bayesian learning and the relevance vector machine. The journal of machine learning research, vol 1, pp 211--244. JMLR. org. cit 3213. [perceptron] pdf google scholar
Yoav Freund and Robert E Schapire. 1999. Large margin classification using the perceptron algorithm. Machine learning, vol 37, no 3, pp 277--296. Springer. cit 868. [perceptron] pdf annote google scholar
Thilo-Thomas Frieß, Nello Cristianini and Colin Campbell. 1998. The kernel-adatron algorithm: a fast and simple learning procedure for support vector machines. In Machine Learning: Proceedings of the Fifteenth International Conference (ICML'98), pp 188--196. Citeseer. cit 352. [perceptron] google scholar
Alexander Johannes Smola. 1998. Learning with Kernels. Technische Universitat Berlin. PhD Thesis. [perceptron] pdf google scholar
F. Rosenblatt. 1958. The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain. Psychological Review, vol 65, no 6, pp 386--408. cit 4607. [ML, perceptron] pdf google scholar

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