Ron Meir

Ronny Meir

2008
49	EE	Dotan Di Castro, Dmitry Volkinshtein, Ron Meir: Temporal Difference Based Actor Critic Learning - Convergence and Neural Implementation. NIPS 2008: 385-392
48	EE	Dori Peleg, Ron Meir: A bilinear formulation for vector sparsity optimization. Signal Processing 88(2): 375-389 (2008)
2007
47	EE	Omer Bobrowski, Ron Meir, Shy Shoham, Yonina C. Eldar: A neural network implementing optimal state estimation based on dynamic spike train decoding. NIPS 2007
46	EE	Dorit Baras, Ron Meir: Reinforcement Learning, Spike-Time-Dependent Plasticity, and the BCM Rule. Neural Computation 19(8): 2245-2279 (2007)
45	EE	Igor Zingman, Ron Meir, Ran El-Yaniv: Size-density spectra and their application to image classification. Pattern Recognition 40(12): 3336-3348 (2007)
2005
44		Peter Auer, Ron Meir: Learning Theory, 18th Annual Conference on Learning Theory, COLT 2005, Bertinoro, Italy, June 27-30, 2005, Proceedings Springer 2005
43	EE	Yaakov Engel, Shie Mannor, Ron Meir: Reinforcement learning with Gaussian processes. ICML 2005: 201-208
42	EE	George Leifman, Ron Meir, Ayellet Tal: Semantic-oriented 3d shape retrieval using relevance feedback. The Visual Computer 21(8-10): 865-875 (2005)
2004
41	EE	Arik Azran, Ron Meir: Data Dependent Risk Bounds for Hierarchical Mixture of Experts Classifiers. COLT 2004: 427-441
40	EE	Dori Peleg, Ron Meir: A Feature Selection Algorithm Based on the Global Minimization of a Generalization Error Bound. NIPS 2004
39	EE	Philip Derbeko, Ran El-Yaniv, Ron Meir: Explicit Learning Curves for Transduction and Application to Clustering and Compression Algorithms. J. Artif. Intell. Res. (JAIR) 22: 117-142 (2004)
2003
38	EE	Ilya Desyatnikov, Ron Meir: Data-Dependent Bounds for Multi-category Classification Based on Convex Losses. COLT 2003: 159-172
37		Yaakov Engel, Shie Mannor, Ron Meir: Bayes Meets Bellman: The Gaussian Process Approach to Temporal Difference Learning. ICML 2003: 154-161
36	EE	Philip Derbeko, Ran El-Yaniv, Ron Meir: Error Bounds for Transductive Learning via Compression and Clustering. NIPS 2003
35	EE	Mordechai Nisenson, Ido Yariv, Ran El-Yaniv, Ron Meir: Towards Behaviometric Security Systems: Learning to Identify a Typist. PKDD 2003: 363-374
34	EE	Shie Mannor, Ron Meir, Tong Zhang: Greedy Algorithms for Classification -- Consistency, Convergence Rates, and Adaptivity. Journal of Machine Learning Research 4: 713-741 (2003)
33	EE	Ron Meir, Tong Zhang: Generalization Error Bounds for Bayesian Mixture Algorithms. Journal of Machine Learning Research 4: 839-860 (2003)
2002
32	EE	Shie Mannor, Ron Meir, Tong Zhang: The Consistency of Greedy Algorithms for Classification. COLT 2002: 319-333
31	EE	Philip Derbeko, Ran El-Yaniv, Ron Meir: Variance Optimized Bagging. ECML 2002: 60-71
30	EE	Yaakov Engel, Shie Mannor, Ron Meir: Sparse Online Greedy Support Vector Regression. ECML 2002: 84-96
29	EE	Ron Meir, Gunnar Rätsch: An Introduction to Boosting and Leveraging. Machine Learning Summer School 2002: 118-183
28	EE	Ron Meir, Tong Zhang: Data-Dependent Bounds for Bayesian Mixture Methods. NIPS 2002: 319-326
27		Shie Mannor, Ron Meir: On the Existence of Linear Weak Learners and Applications to Boosting. Machine Learning 48(1-3): 219-251 (2002)
2001
26	EE	Shie Mannor, Ron Meir: Geometric Bounds for Generalization in Boosting. COLT/EuroCOLT 2001: 461-472
25		Vitaly Maiorov, Ron Meir: Lower bounds for multivariate approximation by affine-invariant dictionaries. IEEE Transactions on Information Theory 47(4): 1569-1575 (2001)
24	EE	Amir Karniel, Ron Meir, Gideon F. Inbar: Best estimated inverse versus inverse of the best estimator. Neural Networks 14(9): 1153-1159 (2001)
23	EE	Amir Karniel, Ron Meir, Gideon F. Inbar: Polyhedral mixture of linear experts for many-to-one mapping inversion and multiple controllers. Neurocomputing 37(1-4): 31-49 (2001)
2000
22		Ron Meir, Ran El-Yaniv, Shai Ben-David: Localized Boosting. COLT 2000: 190-199
21		Shie Mannor, Ron Meir: Weak Learners and Improved Rates of Convergence in Boosting. NIPS 2000: 280-286
20	EE	Vitaly Maiorov, Ron Meir: On the near optimality of the stochastic approximation of smooth functions by neural networks. Adv. Comput. Math. 13(1): 79-103 (2000)
19		Ron Meir: Nonparametric Time Series Prediction Through Adaptive Model Selection. Machine Learning 39(1): 5-34 (2000)
1999
18		Ron Meir, Vitaly Maiorov: Distortion bounds for vector quantizers with finite codebook size. IEEE Transactions on Information Theory 45(5): 1621-1631 (1999)
1998
17	EE	Amir Karniel, Ron Meir, Gideon F. Inbar: Polyhedral mixture of linear experts for many-to-one mapping inversion. ESANN 1998: 155-160
16	EE	Peter L. Bartlett, Vitaly Maiorov, Ron Meir: Almost Linear VC Dimension Bounds for Piecewise Polynomial Networks. NIPS 1998: 190-196
15	EE	Ron Meir, Vitaly Maiorov: On the Optimality of Incremental Neural Network Algorithms. NIPS 1998: 295-301
14		Assaf J. Zeevi, Ron Meir, Vitaly Maiorov: Error Bounds for Functional Approximation and Estimation Using Mixtures of Experts. IEEE Transactions on Information Theory 44(3): 1010-1025 (1998)
13		Peter L. Bartlett, Vitaly Maiorov, Ron Meir: Almost Linear VC-Dimension Bounds for Piecewise Polynomial Networks. Neural Computation 10(8): 2159-2173 (1998)
1997
12	EE	Ron Meir: Performance Bounds for Nonlinear Time Series Prediction. COLT 1997: 122-129
11		Ron Meir: Structural Risk Minimization for Nonparametric Time Series Prediction. NIPS 1997
10	EE	Assaf J. Zeevi, Ronny Meir: Density Estimation Through Convex Combinations of Densities: Approximation and Estimation Bounds. Neural Networks 10(1): 99-109 (1997)
1996
9	EE	Joel Ratsaby, Ron Meir, Vitaly Maiorov: Towards Robust Model Selection Using Estimation and Approximation Error Bounds. COLT 1996: 57-67
8	EE	Assaf J. Zeevi, Ron Meir, Robert J. Adler: Time Series Prediction using Mixtures of Experts. NIPS 1996: 309-318
1995
7		Ronny Meir, Neri Merhav: On the Stochastic Complexity of Learning Realizable and Unrealizable Rules. Machine Learning 19(3): 241-261 (1995)
1994
6	EE	Ronny Meir: Bias, Variance and the Combination of Least Squares Estimators. NIPS 1994: 295-302
1992
5	EE	Ronny Meir, José F. Fontanari: On Learning Noisy Threshold Functions with Finite Precision Weights. COLT 1992: 280-286
4	EE	Joshua Alspector, Ronny Meir, B. Yuhas, Anthony Jayakumar, D. Lippe: A Parallel Gradient Descent Method for Learning in Analog VLSI Neural Networks. NIPS 1992: 836-844
1991
3	EE	Ronny Meir: On Deriving Deterministic Learning Rules from Stochastic Systems. Int. J. Neural Syst. 2(4): 283-289 (1991)
1990
2	EE	Joshua Alspector, Robert B. Allen, Anthony Jayakumar, Torsten Zeppelfeld, Ronny Meir: Relaxation Networks for Large Supervised Learning Problems. NIPS 1990: 1015-1021
1988
1	EE	Tal Grossman, Ronny Meir, Eytan Domany: Learning by Choice of Internal Representations. NIPS 1988: 73-80

Coauthor Index

1	Robert J. Adler	[8]
2	Robert B. Allen	[2]
3	Joshua Alspector	[2] [4]
4	Peter Auer	[44]
5	Arik Azran	[41]
6	Dorit Baras	[46]
7	Peter L. Bartlett	[13] [16]
8	Shai Ben-David	[22]
9	Omer Bobrowski	[47]
10	Dotan Di Castro	[49]
11	Philip Derbeko	[31] [36] [39]
12	Ilya Desyatnikov	[38]
13	Eytan Domany	[1]
14	Ran El-Yaniv	[22] [31] [35] [36] [39] [45]
15	Yonina C. Eldar	[47]
16	Yaakov Engel	[30] [37] [43]
17	José F. Fontanari	[5]
18	Tal Grossman	[1]
19	Gideon F. Inbar	[17] [23] [24]
20	Anthony Jayakumar	[2] [4]
21	Amir Karniel	[17] [23] [24]
22	George Leifman	[42]
23	D. Lippe	[4]
24	Vitaly Maiorov	[9] [13] [14] [15] [16] [18] [20] [25]
25	Shie Mannor	[21] [26] [27] [30] [32] [34] [37] [43]
26	Neri Merhav	[7]
27	Mordechai Nisenson	[35]
28	Dori Peleg	[40] [48]
29	Joel Ratsaby	[9]
30	Gunnar Rätsch	[29]
31	Shy Shoham	[47]
32	Ayellet Tal	[42]
33	Dmitry Volkinshtein	[49]
34	Ido Yariv	[35]
35	B. Yuhas	[4]
36	Assaf J. Zeevi	[8] [10] [14]
37	Torsten Zeppelfeld	[2]
38	Tong Zhang	[28] [32] [33] [34]
39	Igor Zingman	[45]

Colors in the list of coauthors