Towards Representation Independence in PAC Learning
暂无分享,去创建一个
[1] Thomas M. Cover,et al. Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition , 1965, IEEE Trans. Electron. Comput..
[2] J. M. Barzdin,et al. Prognostication of Automata and Functions , 1971, IFIP Congress.
[3] Alfred V. Aho,et al. The Design and Analysis of Computer Algorithms , 1974 .
[4] Karlis Podnieks. Comparing various concepts of function prediction. Part 1. , 1974 .
[5] Manuel Blum,et al. Toward a Mathematical Theory of Inductive Inference , 1975, Inf. Control..
[6] John Gill,et al. Computational Complexity of Probabilistic Turing Machines , 1977, SIAM J. Comput..
[7] Jeffrey D. Ullman,et al. Introduction to Automata Theory, Languages and Computation , 1979 .
[8] Andrew Chi-Chih Yao,et al. Theory and application of trapdoor functions , 1982, 23rd Annual Symposium on Foundations of Computer Science (sfcs 1982).
[9] Vladimir Vapnik,et al. Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics) , 1982 .
[10] Carl H. Smith,et al. Inductive Inference: Theory and Methods , 1983, CSUR.
[11] D. Pollard. Convergence of stochastic processes , 1984 .
[12] R. Dudley. A course on empirical processes , 1984 .
[13] Leslie G. Valiant,et al. A theory of the learnable , 1984, CACM.
[14] École d'été de probabilités de Saint-Flour,et al. École d'Été de Probabilités de Saint-Flour XII - 1982 , 1984 .
[15] Leonid A. Levin,et al. One-way functions and pseudorandom generators , 1985, STOC '85.
[16] Silvio Micali,et al. How to construct random functions , 1986, JACM.
[17] David Haussler,et al. Epsilon-nets and simplex range queries , 1986, SCG '86.
[18] Ronald L. Rivest,et al. Learning decision lists , 2004, Machine Learning.
[19] Leslie G. Valiant,et al. On the learnability of Boolean formulae , 1987, STOC.
[20] N. Littlestone. Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm , 1987, 28th Annual Symposium on Foundations of Computer Science (sfcs 1987).
[21] David Haussler,et al. ɛ-nets and simplex range queries , 1987, Discret. Comput. Geom..
[22] Ronald L. Rivest,et al. Training a 3-node neural network is NP-complete , 1988, COLT '88.
[23] Leonard Pitt,et al. Reductions among prediction problems: on the difficulty of predicting automata , 1988, [1988] Proceedings. Structure in Complexity Theory Third Annual Conference.
[24] Leslie G. Valiant,et al. Computational limitations on learning from examples , 1988, JACM.
[25] David Haussler,et al. Learning decision trees from random examples , 1988, COLT '88.
[26] Leslie G. Valiant,et al. A general lower bound on the number of examples needed for learning , 1988, COLT '88.
[27] David Haussler,et al. Equivalence of models for polynomial learnability , 1988, COLT '88.
[28] Hugo Krawczyk,et al. On the Existence of Pseudorandom Generators , 1988, CRYPTO.
[29] M. Kearns,et al. Crytographic limitations on learning Boolean formulae and finite automata , 1989, STOC '89.
[30] B. K. Natarajan,et al. Some results on learning , 1989 .
[31] D. Haussler. Generalizing the PAC model: sample size bounds from metric dimension-based uniform convergence results , 1989, 30th Annual Symposium on Foundations of Computer Science.
[32] Jeffrey Scott Vitter,et al. Complexity issues in learning by neural nets , 1989, COLT '89.
[33] David Haussler,et al. Learnability and the Vapnik-Chervonenkis dimension , 1989, JACM.
[34] Manfred K. Warmuth,et al. Learning nested differences of intersection-closed concept classes , 2004, Machine Learning.
[35] Leonard Pitt,et al. A polynomial-time algorithm for learning k-variable pattern languages from examples , 1989, COLT '89.
[36] Robert E. Schapire,et al. The strength of weak learnability , 1990, Mach. Learn..