Sample compression schemes for VC classes
暂无分享,去创建一个
[1] J. Neumann. Zur Theorie der Gesellschaftsspiele , 1928 .
[2] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .
[3] Shay Moran,et al. Teaching and compressing for low VC-dimension , 2015, Electron. Colloquium Comput. Complex..
[4] David Haussler,et al. Learnability and the Vapnik-Chervonenkis dimension , 1989, JACM.
[5] Manfred K. Warmuth,et al. Unlabeled Compression Schemes for Maximum Classes, , 2007, COLT.
[6] Aranyak Mehta,et al. Playing large games using simple strategies , 2003, EC '03.
[7] Manfred K. Warmuth,et al. Relating Data Compression and Learnability , 2003 .
[8] Balas K. Natarajan,et al. On learning sets and functions , 2004, Machine Learning.
[9] Leslie G. Valiant,et al. Cryptographic Limitations on Learning Boolean Formulae and Finite Automata , 1993, Machine Learning: From Theory to Applications.
[10] Yi Li,et al. Improved bounds on the sample complexity of learning , 2000, SODA '00.
[11] Sally Floyd,et al. Space-bounded learning and the Vapnik-Chervonenkis dimension , 1989, COLT '89.
[12] R. Schapire. The Strength of Weak Learnability , 1990, Machine Learning.
[13] Boting Yang,et al. Generalizing Labeled and Unlabeled Sample Compression to Multi-label Concept Classes , 2014, ALT.
[14] Manfred K. Warmuth. Compressing to VC Dimension Many Points , 2003, COLT.
[15] R. Dudley. Universal Donsker Classes and Metric Entropy , 1987 .
[16] Umesh V. Vazirani,et al. An Introduction to Computational Learning Theory , 1994 .
[17] Leslie G. Valiant,et al. A general lower bound on the number of examples needed for learning , 1988, COLT '88.
[18] Tong Zhang,et al. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods , 2001, AI Mag..
[19] Vladimir Vapnik,et al. Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities , 1971 .
[20] M. Talagrand. Sharper Bounds for Gaussian and Empirical Processes , 1994 .
[21] Manfred K. Warmuth,et al. Learning integer lattices , 1990, COLT '90.
[22] Peter L. Bartlett,et al. Shifting: One-inclusion mistake bounds and sample compression , 2009, J. Comput. Syst. Sci..
[23] Steve Hanneke,et al. The Optimal Sample Complexity of PAC Learning , 2015, J. Mach. Learn. Res..
[24] Alex M. Andrew,et al. Boosting: Foundations and Algorithms , 2012 .
[25] Isabelle Guyon,et al. An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..
[26] Shai Ben-David,et al. Combinatorial Variability of Vapnik-chervonenkis Classes with Applications to Sample Compression Schemes , 1998, Discret. Appl. Math..
[27] Richard J. Lipton,et al. Simple strategies for large zero-sum games with applications to complexity theory , 1994, STOC '94.
[28] P. Assouad. Densité et dimension , 1983 .
[29] M. Dufwenberg. Game theory. , 2011, Wiley interdisciplinary reviews. Cognitive science.
[30] Roi Livni,et al. Honest Compressions and Their Application to Compression Schemes , 2013, COLT.
[31] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.
[32] Philip M. Long,et al. Characterizations of Learnability for Classes of {0, ..., n}-Valued Functions , 1995, J. Comput. Syst. Sci..
[33] Pierre Simon,et al. Externally definable sets and dependent pairs II , 2012, 1202.2650.
[34] Shai Ben-David,et al. Understanding Machine Learning: From Theory to Algorithms , 2014 .
[35] Amit Daniely,et al. Optimal learners for multiclass problems , 2014, COLT.
[36] Dan Suciu,et al. Journal of the ACM , 2006 .
[37] Yoav Freund,et al. Boosting a weak learning algorithm by majority , 1995, COLT '90.
[38] Leslie G. Valiant,et al. A theory of the learnable , 1984, STOC '84.
[39] Benjamin I. P. Rubinstein,et al. A Geometric Approach to Sample Compression , 2009, J. Mach. Learn. Res..
[40] Manfred K. Warmuth,et al. Sample compression, learnability, and the Vapnik-Chervonenkis dimension , 1995, Machine Learning.
[41] Yoav Freund,et al. Boosting: Foundations and Algorithms , 2012 .