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ECAI
2004
Springer
15 years 5 months ago
Learning Complex and Sparse Events in Long Sequences
The Hierarchical Hidden Markov Model (HHMM) is a well formalized tool suitable to model complex patterns in long temporal or spatial sequences. Even if effective algorithms are ava...
Marco Botta, Ugo Galassi, Attilio Giordana
DATE
2003
IEEE
82views Hardware» more  DATE 2003»
15 years 5 months ago
A Circuit SAT Solver With Signal Correlation Guided Learning
— Boolean Satistifiability has attracted tremendous research effort in recent years, resulting in the developments of various efficient SAT solver packages. Based upon their de...
Feng Lu, Li-C. Wang, Kwang-Ting Cheng, Ric C.-Y. H...
PRICAI
2000
Springer
15 years 3 months ago
Generating Hierarchical Structure in Reinforcement Learning from State Variables
This paper presents the CQ algorithm which decomposes and solves a Markov Decision Process (MDP) by automatically generating a hierarchy of smaller MDPs using state variables. The ...
Bernhard Hengst
UAI
2001
15 years 1 months ago
Improved learning of Bayesian networks
The search space of Bayesian Network structures is usually defined as Acyclic Directed Graphs (DAGs) and the search is done by local transformations of DAGs. But the space of Baye...
Tomás Kocka, Robert Castelo
ICDM
2009
IEEE
149views Data Mining» more  ICDM 2009»
15 years 6 months ago
Accelerated Gradient Method for Multi-task Sparse Learning Problem
—Many real world learning problems can be recast as multi-task learning problems which utilize correlations among different tasks to obtain better generalization performance than...
Xi Chen, Weike Pan, James T. Kwok, Jaime G. Carbon...