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ICML
1998
IEEE
14 years 8 months ago
Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions
This paper introduces a new algorithm, Q2, foroptimizingthe expected output ofamultiinput noisy continuous function. Q2 is designed to need only a few experiments, it avoids stron...
Andrew W. Moore, Jeff G. Schneider, Justin A. Boya...
IPSN
2009
Springer
14 years 2 months ago
Simultaneous placement and scheduling of sensors
We consider the problem of monitoring spatial phenomena, such as road speeds on a highway, using wireless sensors with limited battery life. A central question is to decide where ...
Andreas Krause, Ram Rajagopal, Anupam Gupta, Carlo...
PKDD
2009
Springer
181views Data Mining» more  PKDD 2009»
14 years 2 months ago
Active Learning for Reward Estimation in Inverse Reinforcement Learning
Abstract. Inverse reinforcement learning addresses the general problem of recovering a reward function from samples of a policy provided by an expert/demonstrator. In this paper, w...
Manuel Lopes, Francisco S. Melo, Luis Montesano
KDD
2002
ACM
93views Data Mining» more  KDD 2002»
14 years 7 months ago
Interactive deduplication using active learning
Deduplication is a key operation in integrating data from multiple sources. The main challenge in this task is designing a function that can resolve when a pair of records refer t...
Sunita Sarawagi, Anuradha Bhamidipaty
ESANN
2003
13 years 8 months ago
Approximation of Function by Adaptively Growing Radial Basis Function Neural Networks
In this paper a neural network for approximating function is described. The activation functions of the hidden nodes are the Radial Basis Functions (RBF) whose parameters are learn...
Jianyu Li, Siwei Luo, Yingjian Qi