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ICDM
2010
IEEE
168views Data Mining» more  ICDM 2010»
13 years 5 months ago
Anomaly Detection Using an Ensemble of Feature Models
We present a new approach to semi-supervised anomaly detection. Given a set of training examples believed to come from the same distribution or class, the task is to learn a model ...
Keith Noto, Carla E. Brodley, Donna K. Slonim
ICANN
2003
Springer
14 years 1 months ago
Neural Network Ensemble with Negatively Correlated Features for Cancer Classification
The development of microarray technology has supplied a large volume of data to many fields. In particular, it has been applied to prediction and diagnosis of cancer, so that it ex...
Hong-Hee Won, Sung-Bae Cho
ICALT
2007
IEEE
14 years 2 months ago
Geographical Concept Recognition With the Octgrid Method for Learning Geography and Geology
Recognizing geographical concepts such as ridges, valleys, and contour lines is an important issue in geography learning. We provide a system for automatic recognition of such con...
Ryusuke Yokoyama, Akira Kureha, Tomoe Motohashi, H...
ECCV
2006
Springer
13 years 11 months ago
Comparing Ensembles of Learners: Detecting Prostate Cancer from High Resolution MRI
While learning ensembles have been widely used for various pattern recognition tasks, surprisingly, they have found limited application in problems related to medical image analysi...
Anant Madabhushi, Jianbo Shi, Michael D. Feldman, ...
ARTMED
2002
119views more  ARTMED 2002»
13 years 7 months ago
Lung cancer cell identification based on artificial neural network ensembles
An artificial neural network ensemble is a learning paradigm where several artificial neural networks are jointly used to solve a problem. In this paper, an automatic pathological...
Zhi-Hua Zhou, Yuan Jiang, Yu-Bin Yang, Shifu Chen