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Feature Augmentation via Nonparametrics and Selection (FANS) in High Dimensional Classification
We motivate FANS by generalizing the Naive Bayes model, writing the log ratio of joint densities as a linear combination of those of marginal densities.
Regularization for Cox's proportional hazards model with NP-dimensionality
High throughput genetic sequencing arrays with thousands of measurements per sample and a great amount of related censored clinical data have increased demanding need for better measurement specific model selection.
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