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» Approximability of Probability Distributions
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KDD
2004
ACM
137views Data Mining» more  KDD 2004»
16 years 4 days ago
Mining scale-free networks using geodesic clustering
Many real-world graphs have been shown to be scale-free— vertex degrees follow power law distributions, vertices tend to cluster, and the average length of all shortest paths is...
Andrew Y. Wu, Michael Garland, Jiawei Han
CDC
2009
IEEE
132views Control Systems» more  CDC 2009»
15 years 11 months ago
Q-learning and Pontryagin's Minimum Principle
Abstract— Q-learning is a technique used to compute an optimal policy for a controlled Markov chain based on observations of the system controlled using a non-optimal policy. It ...
Prashant G. Mehta, Sean P. Meyn
EUROCRYPT
1999
Springer
15 years 11 months ago
An Analysis of Exponentiation Based on Formal Languages
A recoding rule for exponentiation is a method for reducing the cost of the exponentiation ae by reducing the number of required multiplications. If w(e) is the (hamming) weight of...
Luke O'Connor
VISUALIZATION
1997
IEEE
15 years 11 months ago
Multiresolution tetrahedral framework for visualizing regular volume data
We present a multiresolution framework, called Multi-Tetra framework, that approximates volume data with different levelsof-detail tetrahedra. The framework is generated through a...
Yong Zhou, Baoquan Chen, Arie E. Kaufman
NN
1997
Springer
174views Neural Networks» more  NN 1997»
15 years 11 months ago
Learning Dynamic Bayesian Networks
Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. Many time series models, including the hidden Markov models (H...
Zoubin Ghahramani