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» Optimizing abstaining classifiers using ROC analysis
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ICPR
2004
IEEE
16 years 3 months ago
Morphology Analysis of Physiological Signals Using Hidden Markov Models
We describe a clustering algorithm based on continuous Hidden Markov Models (HMM) to automatically classify both electrocardiogram (ECG) and intracranial pressure (ICP) beats base...
Daniel Novák, Lenka Lhotská, David C...
HCI
2009
15 years 18 days ago
Mind-Mirror: EEG-Guided Image Evolution
Abstract. We propose a brain-computer interface (BCI) system for evolving images in realtime based on subject feedback derived from electroencephalography (EEG). The goal of this s...
Nima Bigdely Shamlo, Scott Makeig
PRL
2010
209views more  PRL 2010»
14 years 9 months ago
Efficient update of the covariance matrix inverse in iterated linear discriminant analysis
For fast classification under real-time constraints, as required in many imagebased pattern recognition applications, linear discriminant functions are a good choice. Linear discr...
Jan Salmen, Marc Schlipsing, Christian Igel
NIPS
2003
15 years 4 months ago
AUC Optimization vs. Error Rate Minimization
The area under an ROC curve (AUC) is a criterion used in many applications to measure the quality of a classification algorithm. However, the objective function optimized in most...
Corinna Cortes, Mehryar Mohri
GECCO
2006
Springer
140views Optimization» more  GECCO 2006»
15 years 6 months ago
A representational ecology for learning classifier systems
The representation used by a learning algorithm introduces a bias which is more or less well-suited to any given learning problem. It is well known that, across all possible probl...
James A. R. Marshall, Tim Kovacs