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NIPS
2000
15 years 5 months ago
A PAC-Bayesian Margin Bound for Linear Classifiers: Why SVMs work
We investigate how the normalization of vectors influences the result of SVMs. 1 Normalization For the theoretical background, please refer to [1]. 2 Experiments We empirically co...
Ralf Herbrich, Thore Graepel
PODS
2009
ACM
100views Database» more  PODS 2009»
16 years 4 months ago
Space-optimal heavy hitters with strong error bounds
The problem of finding heavy hitters and approximating the frequencies of items is at the heart of many problems in data stream analysis. It has been observed that several propose...
Radu Berinde, Graham Cormode, Piotr Indyk, Martin ...
CORR
2008
Springer
125views Education» more  CORR 2008»
15 years 4 months ago
Adversary lower bounds for nonadaptive quantum algorithms
We present two general methods for proving lower bounds on the query complexity of nonadaptive quantum algorithms. Both methods are based on the adversary method of Ambainis. We sh...
Pascal Koiran, Jürgen Landes, Natacha Portier...
ICRA
2007
IEEE
162views Robotics» more  ICRA 2007»
15 years 10 months ago
Learning and Evaluation of the Approach Vector for Automatic Grasp Generation and Planning
— In this paper, we address the problem of automatic grasp generation for robotic hands where experience and shape primitives are used in synergy so to provide a basis not only f...
Staffan Ekvall, Danica Kragic
ICML
2004
IEEE
16 years 4 months ago
Co-EM support vector learning
Multi-view algorithms, such as co-training and co-EM, utilize unlabeled data when the available attributes can be split into independent and compatible subsets. Co-EM outperforms ...
Ulf Brefeld, Tobias Scheffer