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ML
2002
ACM
178views Machine Learning» more  ML 2002»
13 years 7 months ago
Metric-Based Methods for Adaptive Model Selection and Regularization
We present a general approach to model selection and regularization that exploits unlabeled data to adaptively control hypothesis complexity in supervised learning tasks. The idea ...
Dale Schuurmans, Finnegan Southey
BMCBI
2008
114views more  BMCBI 2008»
13 years 7 months ago
Combining classifiers for improved classification of proteins from sequence or structure
Background: Predicting a protein's structural or functional class from its amino acid sequence or structure is a fundamental problem in computational biology. Recently, there...
Iain Melvin, Jason Weston, Christina S. Leslie, Wi...
BMCBI
2007
130views more  BMCBI 2007»
13 years 7 months ago
Exploiting residue-level and profile-level interface propensities for usage in binding sites prediction of proteins
Background: Recognition of binding sites in proteins is a direct computational approach to the characterization of proteins in terms of biological and biochemical function. Residu...
Qiwen Dong, Xiaolong Wang, Lei Lin, Yi Guan
MLDM
2009
Springer
14 years 2 months ago
Drift-Aware Ensemble Regression
Abstract. Regression models are often required for controlling production processes by predicting parameter values. However, the implicit assumption of standard regression techniqu...
Frank Rosenthal, Peter Benjamin Volk, Martin Hahma...
MSR
2006
ACM
14 years 1 months ago
Predicting defect densities in source code files with decision tree learners
With the advent of open source software repositories the data available for defect prediction in source files increased tremendously. Although traditional statistics turned out t...
Patrick Knab, Martin Pinzger, Abraham Bernstein