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....
—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...
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...
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
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...