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SODA
2000
ACM
85views Algorithms» more  SODA 2000»
13 years 11 months ago
Improved bounds on the sample complexity of learning
We present a new general upper bound on the number of examples required to estimate all of the expectations of a set of random variables uniformly well. The quality of the estimat...
Yi Li, Philip M. Long, Aravind Srinivasan
EMNLP
2010
13 years 8 months ago
Minimum Error Rate Training by Sampling the Translation Lattice
Minimum Error Rate Training is the algorithm for log-linear model parameter training most used in state-of-the-art Statistical Machine Translation systems. In its original formula...
Samidh Chatterjee, Nicola Cancedda
ICML
2010
IEEE
13 years 11 months ago
Efficient Selection of Multiple Bandit Arms: Theory and Practice
We consider the general, widely applicable problem of selecting from n real-valued random variables a subset of size m of those with the highest means, based on as few samples as ...
Shivaram Kalyanakrishnan, Peter Stone
CDC
2008
IEEE
163views Control Systems» more  CDC 2008»
14 years 4 months ago
A cooperative deployment strategy for optimal sampling in spatiotemporal estimation
— This paper considers a network composed of robotic agents and static nodes performing spatial estimation of a dynamic physical processes. The physical process is modeled as a s...
Rishi Graham, Jorge Cortés
ICIC
2005
Springer
14 years 3 months ago
Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning
In recent years, mining with imbalanced data sets receives more and more attentions in both theoretical and practical aspects. This paper introduces the importance of imbalanced da...
Hui Han, Wenyuan Wang, Binghuan Mao