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» Approximation algorithms for co-clustering
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ICML
2001
IEEE
16 years 7 months ago
Off-Policy Temporal Difference Learning with Function Approximation
We introduce the first algorithm for off-policy temporal-difference learning that is stable with linear function approximation. Off-policy learning is of interest because it forms...
Doina Precup, Richard S. Sutton, Sanjoy Dasgupta
SIGECOM
2010
ACM
147views ECommerce» more  SIGECOM 2010»
15 years 11 months ago
Socially desirable approximations for Dodgson's voting rule
In 1876 Charles Lutwidge Dodgson suggested the intriguing voting rule that today bears his name. Although Dodgson’s rule is one of the most well-studied voting rules, it suffers...
Ioannis Caragiannis, Christos Kaklamanis, Nikos Ka...
DAC
2009
ACM
16 years 7 months ago
A fully polynomial time approximation scheme for timing driven minimum cost buffer insertion
As VLSI technology enters the nanoscale regime, interconnect delay has become the bottleneck of the circuit timing. As one of the most powerful techniques for interconnect optimiz...
Shiyan Hu, Zhuo Li, Charles J. Alpert
BIODATAMINING
2008
96views more  BIODATAMINING 2008»
15 years 6 months ago
Fast approximate hierarchical clustering using similarity heuristics
Background: Agglomerative hierarchical clustering (AHC) is a common unsupervised data analysis technique used in several biological applications. Standard AHC methods require that...
Meelis Kull, Jaak Vilo
KDD
2004
ACM
158views Data Mining» more  KDD 2004»
16 years 7 months ago
A generalized maximum entropy approach to bregman co-clustering and matrix approximation
Co-clustering is a powerful data mining technique with varied applications such as text clustering, microarray analysis and recommender systems. Recently, an informationtheoretic ...
Arindam Banerjee, Inderjit S. Dhillon, Joydeep Gho...