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JMLR
2010
102views more  JMLR 2010»
14 years 11 months ago
Unsupervised Supervised Learning I: Estimating Classification and Regression Errors without Labels
Estimating the error rates of classifiers or regression models is a fundamental task in machine learning which has thus far been studied exclusively using supervised learning tech...
Pinar Donmez, Guy Lebanon, Krishnakumar Balasubram...
BMCBI
2006
150views more  BMCBI 2006»
15 years 4 months ago
Instance-based concept learning from multiclass DNA microarray data
Background: Various statistical and machine learning methods have been successfully applied to the classification of DNA microarray data. Simple instance-based classifiers such as...
Daniel P. Berrar, Ian Bradbury, Werner Dubitzky
CSB
2005
IEEE
189views Bioinformatics» more  CSB 2005»
15 years 10 months ago
Learning Yeast Gene Functions from Heterogeneous Sources of Data Using Hybrid Weighted Bayesian Networks
We developed a machine learning system for determining gene functions from heterogeneous sources of data sets using a Weighted Naive Bayesian Network (WNB). The knowledge of gene ...
Xutao Deng, Huimin Geng, Hesham H. Ali
EUROCOLT
1997
Springer
15 years 8 months ago
Ordinal Mind Change Complexity of Language Identification
The approach of ordinal mind change complexity, introduced by Freivalds and Smith, uses (notations for) constructive ordinals to bound the number of mind changes made by a learnin...
Andris Ambainis, Sanjay Jain, Arun Sharma
IJCNN
2008
IEEE
15 years 11 months ago
Learning adaptive subject-independent P300 models for EEG-based brain-computer interfaces
Abstract— This paper proposes an approach to learn subjectindependent P300 models for EEG-based brain-computer interfaces. The P300 models are first learned using a pool of exis...
Shijian Lu, Cuntai Guan, Haihong Zhang