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ICML
1998
IEEE
16 years 5 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
15 years 11 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»
15 years 10 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»
16 years 4 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
15 years 5 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