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AIIA
2015
Springer
3 years 7 months ago
Learning Accurate Cutset Networks by Exploiting Decomposability
The rising interest around tractable Probabilistic Graphical Models is due to the guarantees on inference feasibility they provide. Among them, Cutset Networks (CNets) have recentl...
Nicola Di Mauro, Antonio Vergari, Floriana Esposit...
KDD
2010
ACM
224views Data Mining» more  KDD 2010»
9 years 3 months ago
Multi-label learning by exploiting label dependency
In multi-label learning, each training example is associated with a set of labels and the task is to predict the proper label set for the unseen example. Due to the tremendous (ex...
Min-Ling Zhang, Kun Zhang
GECCO
2009
Springer
188views Optimization» more  GECCO 2009»
9 years 3 months ago
Exploiting multiple classifier types with active learning
Many approaches to active learning involve training one classifier by periodically choosing new data points about which the classifier has the least confidence, but designing a co...
Zhenyu Lu, Josh Bongard
KDD
2010
ACM
272views Data Mining» more  KDD 2010»
9 years 3 months ago
Beyond heuristics: learning to classify vulnerabilities and predict exploits
The security demands on modern system administration are enormous and getting worse. Chief among these demands, administrators must monitor the continual ongoing disclosure of sof...
Mehran Bozorgi, Lawrence K. Saul, Stefan Savage, G...
GECCO
2004
Springer
9 years 5 months ago
Real-Coded Bayesian Optimization Algorithm: Bringing the Strength of BOA into the Continuous World
This paper describes a continuous estimation of distribution algorithm (EDA) to solve decomposable, real-valued optimization problems quickly, accurately, and reliably. This is the...
Chang Wook Ahn, Rudrapatna S. Ramakrishna, David E...
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