Data filtering and distribution modeling algorithms for machine learning
暂无分享,去创建一个
[1] D. Cowling,et al. Assessing the relationship between ad volume and awareness of a tobacco education media campaign , 2010, Tobacco Control.
[2] D. Lindley. On a Measure of the Information Provided by an Experiment , 1956 .
[3] David R. Cox. The analysis of binary data , 1970 .
[4] W. J. Studden,et al. Theory Of Optimal Experiments , 1972 .
[5] Norbert Sauer,et al. On the Density of Families of Sets , 1972, J. Comb. Theory A.
[6] Richard O. Duda,et al. Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.
[7] John W. Tukey,et al. A Projection Pursuit Algorithm for Exploratory Data Analysis , 1974, IEEE Transactions on Computers.
[8] Peter E. Hart,et al. Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.
[9] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[10] Tom Michael Mitchell. Version spaces: an approach to concept learning. , 1979 .
[11] Temple F. Smith. Occam's razor , 1980, Nature.
[12] Ing Rj Ser. Approximation Theorems of Mathematical Statistics , 1980 .
[13] A. Cohen,et al. Finite Mixture Distributions , 1982 .
[14] J J Hopfield,et al. Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.
[15] Carl H. Smith,et al. Inductive Inference: Theory and Methods , 1983, CSUR.
[16] J. Friedman,et al. PROJECTION PURSUIT DENSITY ESTIMATION , 1984 .
[17] Leslie G. Valiant,et al. A theory of the learnable , 1984, STOC '84.
[18] D. Freedman,et al. Asymptotics of Graphical Projection Pursuit , 1984 .
[19] Geoffrey E. Hinton,et al. A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..
[20] Peter Smith. Convexity methods in variational calculus , 1985 .
[21] David Haussler,et al. Classifying learnable geometric concepts with the Vapnik-Chervonenkis dimension , 1986, STOC '86.
[22] Carsten Peterson,et al. A Mean Field Theory Learning Algorithm for Neural Networks , 1987, Complex Syst..
[23] Stuart Geman,et al. Stochastic Relaxation Methods for Image Restoration and Expert Systems , 1988 .
[24] Judea Pearl,et al. Probabilistic reasoning in intelligent systems , 1988 .
[25] Stuart German,et al. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1988 .
[26] David Haussler,et al. Predicting {0,1}-functions on randomly drawn points , 1988, COLT '88.
[27] David Haussler,et al. Equivalence of models for polynomial learnability , 1988, COLT '88.
[28] Erkki Oja,et al. Neural Networks, Principal Components, and Subspaces , 1989, Int. J. Neural Syst..
[29] Steven J. Nowlan,et al. Maximum Likelihood Competitive Learning , 1989, NIPS.
[30] Terence D. Sanger,et al. Optimal unsupervised learning in a single-layer linear feedforward neural network , 1989, Neural Networks.
[31] Ronald L. Graham,et al. Concrete mathematics - a foundation for computer science , 1991 .
[32] David A. Cohn,et al. Training Connectionist Networks with Queries and Selective Sampling , 1989, NIPS.
[33] David Haussler,et al. Learnability and the Vapnik-Chervonenkis dimension , 1989, JACM.
[34] Yoav Freund,et al. Boosting a weak learning algorithm by majority , 1995, COLT '90.
[35] Eric B. Baum,et al. Constructing Hidden Units Using Examples and Queries , 1990, NIPS.
[36] Ronald L. Rivest,et al. On the sample complexity of pac-learning using random and chosen examples , 1990, Annual Conference Computational Learning Theory.
[37] Tomaso A. Poggio,et al. Extensions of a Theory of Networks for Approximation and Learning , 1990, NIPS.
[38] Robert E. Schapire,et al. Efficient distribution-free learning of probabilistic concepts , 1990, Proceedings [1990] 31st Annual Symposium on Foundations of Computer Science.
[39] Wolfgang Kinzel,et al. Improving a Network Generalization Ability by Selecting Examples , 1990 .
[40] David Haussler,et al. Bounds on the sample complexity of Bayesian learning using information theory and the VC dimension , 1991, COLT '91.
[41] Anders Krogh,et al. Introduction to the theory of neural computation , 1994, The advanced book program.
[42] Eric B. Baum,et al. Neural net algorithms that learn in polynomial time from examples and queries , 1991, IEEE Trans. Neural Networks.
[43] Balas K. Natarajan,et al. Machine Learning: A Theoretical Approach , 1992 .
[44] Alexander A. Razborov,et al. Majority gates vs. general weighted threshold gates , 1992, [1992] Proceedings of the Seventh Annual Structure in Complexity Theory Conference.
[45] Harris Drucker,et al. Improving Performance in Neural Networks Using a Boosting Algorithm , 1992, NIPS.
[46] Yoav Freund,et al. An improved boosting algorithm and its implications on learning complexity , 1992, COLT '92.
[47] H. Sebastian Seung,et al. Query by committee , 1992, COLT '92.
[48] Michael Kearns,et al. Efficient noise-tolerant learning from statistical queries , 1993, STOC.
[49] György Turán,et al. Lower bounds for PAC learning with queries , 1993, COLT '93.
[50] Michael Kharitonov,et al. Cryptographic hardness of distribution-specific learning , 1993, STOC.
[51] Leslie G. Valiant,et al. Cryptographic Limitations on Learning Boolean Formulae and Finite Automata , 1993, Machine Learning: From Theory to Applications.
[52] W. Näther. Optimum experimental designs , 1994 .
[53] Manfred K. Warmuth,et al. The Weighted Majority Algorithm , 1994, Inf. Comput..
[54] G. Kane. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol 1: Foundations, vol 2: Psychological and Biological Models , 1994 .
[55] S. Klinke,et al. Exploratory Projection Pursuit , 1995 .