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 |