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ML
2006
ACM
142views Machine Learning» more  ML 2006»
13 years 7 months ago
The max-min hill-climbing Bayesian network structure learning algorithm
We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and sea...
Ioannis Tsamardinos, Laura E. Brown, Constantin F....
ICRA
2010
IEEE
81views Robotics» more  ICRA 2010»
13 years 6 months ago
Using recursive spectral registrations to determine brokenness as measure of structural map errors
—There are many common error sources that influence mapping, e.g., salt and pepper noise as well as other effects occurring quite uniformly distributed over the map. On the oth...
Andreas Birk
SIGSOFT
2007
ACM
14 years 8 months ago
State coverage: a structural test adequacy criterion for behavior checking
We propose a new language-independent, structural test adequacy criterion called state coverage. State coverage measures whether unit-level tests check the outputs and side effect...
Ken Koster, David Kao
WWW
2007
ACM
14 years 8 months ago
A kernel based structure matching for web services search
This paper describes a kernel based Web Services (abbreviated as service) matching mechanism for service discovery and integration. The matching mechanism tries to exploit the lat...
Yu Jianjun, Guo Shengmin, Su Hao, Zhang Hui, Xu Ke
SDM
2009
SIAM
202views Data Mining» more  SDM 2009»
14 years 5 months ago
Proximity-Based Anomaly Detection Using Sparse Structure Learning.
We consider the task of performing anomaly detection in highly noisy multivariate data. In many applications involving real-valued time-series data, such as physical sensor data a...
Tsuyoshi Idé, Aurelie C. Lozano, Naoki Abe,...