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David J. Miller

Researcher at Pennsylvania State University

Publications -  494
Citations -  13814

David J. Miller is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Higgs boson & Large Hadron Collider. The author has an hindex of 58, co-authored 489 publications receiving 12902 citations. Previous affiliations of David J. Miller include IBM & University of California, Santa Barbara.

Papers
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BookDOI

Handbook of LHC Higgs Cross Sections: 3. Higgs Properties

N. Moretti, +151 more
TL;DR: In 2012 and the first half of 2013, the LHC Higgs Cross Section Working Group as mentioned in this paper presented the state of the art of Higgs physics at the Large Hadron Collider (LHC), integrating all new results that have appeared in the last few years.
Posted ContentDOI

Handbook of LHC Higgs Cross Sections: 3. Higgs Properties

Sven Heinemeyer, +156 more
TL;DR: In 2012 and the first half of 2013, the LHC Higgs Cross Section Working Group as mentioned in this paper presented the state of the art of Higgs physics at the Large Hadron Collider (LHC), integrating all new results that have appeared in the last few years.
Journal ArticleDOI

Search for neutral MSSM Higgs bosons at LEP

S. Schael, +1282 more
TL;DR: In this paper, four LEP collaborations, ALEPH, DELPHI, L3 and OPAL, have searched for the neutral Higgs bosons which are predicted by the minimal supersymmetric standard model (MSSM).
Journal ArticleDOI

Physics interplay of the LHC and the ILC

Georg Weiglein, +127 more
- 01 Apr 2006 - 
TL;DR: In this paper, the authors discuss the possible interplay between the Large Hadron Collider (LHC) and the International e(+)e(-) Linear Collider (ILC) in testing the Standard Model and in discovering and determining the origin of new physics.
Proceedings Article

A Mixture of Experts Classifier with Learning Based on Both Labelled and Unlabelled Data

TL;DR: A classifier structure and learning algorithm that make effective use of unlabelled data to improve performance and is a "mixture of experts" structure that is equivalent to the radial basis function (RBF) classifier, but unlike RBFs, is amenable to likelihood-based training.