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ROBUSTNESS IN EXPERIMENTAL DESIGN: A STUDY ON THE RELIABILITY OF SELECTION APPROACHES

Stefan Brandmaier, Igor V Tetko
2013 Computational and Structural Biotechnology Journal  
Acknowledgements This study was partially supported by the FP7 project "CAse studies on the development and application of in-silica techniques for environmental hazard and risk assessment" (  ...  The observance that PLS-Optimal has a lower variability in selection than DescRep is coherent as the D-Optimal criterion also has lower variability than MDC.  ...  The latent variables in our implementation are derived from a PLS model on the pre-selected compounds.  ... 
doi:10.5936/csbj.201305002 pmid:24688738 pmcid:PMC3962228 fatcat:idtfxyq74zfsth5mmi2g5gc6cm

Efficient computations via scalable sparse kernel partial least squares and boosted latent features

Michinari Momma
2005 Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining - KDD '05  
Kernel boosted latent features (KBLF) is a variant of KPLS for any differentiable loss functions.  ...  The algorithm provides an interesting "path" from a maximum residual criterion based algorithm with orthogonality conditions to the dense KPLS/KBLF.  ...  or another way of optimizing the coefficient based on the loss function.  ... 
doi:10.1145/1081870.1081952 dblp:conf/kdd/Momma05 fatcat:colpqr62bvd7tntekj3lm3z2hq