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SOFSEM
2010
Springer
14 years 5 months ago
Regret Minimization and Job Scheduling
Regret minimization has proven to be a very powerful tool in both computational learning theory and online algorithms. Regret minimization algorithms can guarantee, for a single de...
Yishay Mansour
ICML
2004
IEEE
14 years 9 months ago
SVM-based generalized multiple-instance learning via approximate box counting
The multiple-instance learning (MIL) model has been very successful in application areas such as drug discovery and content-based imageretrieval. Recently, a generalization of thi...
Qingping Tao, Stephen D. Scott, N. V. Vinodchandra...
KDD
2006
ACM
163views Data Mining» more  KDD 2006»
14 years 8 months ago
New EM derived from Kullback-Leibler divergence
We introduce a new EM framework in which it is possible not only to optimize the model parameters but also the number of model components. A key feature of our approach is that we...
Longin Jan Latecki, Marc Sobel, Rolf Lakämper
PODS
2007
ACM
196views Database» more  PODS 2007»
14 years 8 months ago
On the complexity of managing probabilistic XML data
In [3], we introduced a framework for querying and updating probabilistic information over unordered labeled trees, the probabilistic tree model. The data model is based on trees ...
Pierre Senellart, Serge Abiteboul
GECCO
2007
Springer
180views Optimization» more  GECCO 2007»
14 years 2 months ago
Exploiting multiple robots to accelerate self-modeling
In previous work [8] a computational framework was demonstrated that allows a mobile robot to autonomously evolve models its own body for the purposes of adaptive behavior generat...
Josh C. Bongard