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ICML
2007
IEEE
16 years 4 months ago
Bottom-up learning of Markov logic network structure
Markov logic networks (MLNs) are a statistical relational model that consists of weighted firstorder clauses and generalizes first-order logic and Markov networks. The current sta...
Lilyana Mihalkova, Raymond J. Mooney
KDD
2009
ACM
192views Data Mining» more  KDD 2009»
16 years 4 months ago
Time series shapelets: a new primitive for data mining
Classification of time series has been attracting great interest over the past decade. Recent empirical evidence has strongly suggested that the simple nearest neighbor algorithm ...
Lexiang Ye, Eamonn J. Keogh
CVPR
2010
IEEE
16 years 14 days ago
Fast Globally Optimal 2D Human Detection with Loopy Graph Models
This paper presents an algorithm for recovering the globally optimal 2D human figure detection using a loopy graph model. This is computationally challenging because the time comp...
Tai-Peng Tian, Stan Sclaroff
GECCO
2007
Springer
155views Optimization» more  GECCO 2007»
15 years 10 months ago
Solving the MAXSAT problem using a multivariate EDA based on Markov networks
Markov Networks (also known as Markov Random Fields) have been proposed as a new approach to probabilistic modelling in Estimation of Distribution Algorithms (EDAs). An EDA employ...
Alexander E. I. Brownlee, John A. W. McCall, Deryc...
CIKM
2004
Springer
15 years 9 months ago
Document clustering based on cluster validation
This paper presents a cluster validation based document clustering algorithm, which is capable of identifying both important feature words and true model order (cluster number). I...
Zheng-Yu Niu, Dong-Hong Ji, Chew Lim Tan