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192
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SDM
2008
SIAM
138views Data Mining» more  SDM 2008»
15 years 8 months ago
Learning Markov Network Structure using Few Independence Tests
In this paper we present the Dynamic Grow-Shrink Inference-based Markov network learning algorithm (abbreviated DGSIMN), which improves on GSIMN, the state-ofthe-art algorithm for...
Parichey Gandhi, Facundo Bromberg, Dimitris Margar...
172
Voted
ECCV
2000
Springer
16 years 8 months ago
Significantly Different Textures: A Computational Model of Pre-attentive Texture Segmentation
Abstract. Recent human vision research [1] suggests modelling preattentive texture segmentation by taking a set of feature samples from a local region on each side of a hypothesize...
Ruth Rosenholz
217
Voted
KDD
2004
ACM
135views Data Mining» more  KDD 2004»
16 years 7 months ago
Discovering additive structure in black box functions
Many automated learning procedures lack interpretability, operating effectively as a black box: providing a prediction tool but no explanation of the underlying dynamics that driv...
Giles Hooker
298
Voted
SIGMOD
2008
ACM
191views Database» more  SIGMOD 2008»
16 years 7 months ago
Efficient aggregation for graph summarization
Graphs are widely used to model real world objects and their relationships, and large graph datasets are common in many application domains. To understand the underlying character...
Yuanyuan Tian, Richard A. Hankins, Jignesh M. Pate...
229
Voted
DCOSS
2007
Springer
16 years 1 months ago
Separating the Wheat from the Chaff: Practical Anomaly Detection Schemes in Ecological Applications of Distributed Sensor Networ
Abstract. We develop a practical, distributed algorithm to detect events, identify measurement errors, and infer missing readings in ecological applications of wireless sensor netw...
Luís M. A. Bettencourt, Aric A. Hagberg, Le...