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IPMI
2011
Springer
8 years 10 months ago
Generalized Sparse Regularization with Application to fMRI Brain Decoding
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...
Bernard Ng, Rafeef Abugharbieh
HCI
2009
9 years 4 months ago
Ensemble SWLDA Classifiers for the P300 Speller
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...
Garett D. Johnson, Dean J. Krusienski
PRL
2006
98views more  PRL 2006»
9 years 6 months ago
Data complexity assessment in undersampled classification of high-dimensional biomedical data
Regularized linear classifiers have been successfully applied in undersampled, i.e. small sample size/high dimensionality biomedical classification problems. Additionally, a desig...
Richard Baumgartner, Ray L. Somorjai
JBI
2008
119views Bioinformatics» more  JBI 2008»
9 years 6 months ago
Decision tool for the early diagnosis of trauma patient hypovolemia
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...
NIPS
1996
9 years 8 months ago
Combinations of Weak Classifiers
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...
Chuanyi Ji, Sheng Ma
FLAIRS
2001
9 years 8 months ago
Decision Tree Rule Reduction Using Linear Classifiers in Multilayer Perceptron
It hasbeenshownthat a neuralnetworkis better thana direct applicationof inductiontrees in modelingcomplex relations of inputattributes in sampledata. We proposethat conciserules b...
DaeEun Kim, Sea Woo Kim
KDD
2005
ACM
117views Data Mining» more  KDD 2005»
10 years 7 months ago
Rule extraction from linear support vector machines
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...
Glenn Fung, Sathyakama Sandilya, R. Bharat Rao
ICML
2008
IEEE
10 years 7 months ago
Confidence-weighted linear classification
We introduce confidence-weighted linear classifiers, which add parameter confidence information to linear classifiers. Online learners in this setting update both classifier param...
Mark Dredze, Koby Crammer, Fernando Pereira
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