Many current medical image analysis problems involve learning thousands or even millions of model parameters from extremely few samples. Employing sparse models provides an effecti...
Abstract. The P300 Speller has proven to be an effective paradigm for braincomputer interface (BCI) communication. Using this paradigm, studies have shown that a simple linear clas...
Regularized linear classifiers have been successfully applied in undersampled, i.e. small sample size/high dimensionality biomedical classification problems. Additionally, a desig...
We present a classifier for use as a decision assist tool to identify a hypovolemic state in trauma patients during helicopter transport to a hospital, when reliable acquisition o...
Liangyou Chen, Thomas M. McKenna, Andrew T. Reisne...
To obtain classification systems with both good generalizat`ion performance and efficiency in space and time, we propose a learning method based on combinations of weak classifiers...
It hasbeenshownthat a neuralnetworkis better thana direct applicationof inductiontrees in modelingcomplex relations of inputattributes in sampledata. We proposethat conciserules b...
We describe an algorithm for converting linear support vector machines and any other arbitrary hyperplane-based linear classifiers into a set of non-overlapping rules that, unlike...
We introduce confidence-weighted linear classifiers, which add parameter confidence information to linear classifiers. Online learners in this setting update both classifier param...