MSNE

AcronymDefinition
MSNEMissouri Society of Newspaper Editors
MSNEMaster of Science in Nuclear Engineering
References in periodicals archive ?
TABLE 1 Equilibrium Averages Predicted by Theory Lemons Differentiation MSNE Quality (supplied) 1 5.5 8.2 [absolute value of [DELTA]Quality] 0 9 2.88 Price (posted) 1 19 12.5 [absolute value of [DELTA]Price] 0 18 5.76 Markup 0 13.5 4.32 Sellers' surplus 0 60 19.2 Buyers' surplus 86 74 116.8 Total surplus 86 134 136 % Efficiency (a) 61.4 95.7 97.1 (a) % Efficiency = Total Surplus/140.
We also compare the proposed MDGPP algorithm with some traditional single-view-based dimensionality reduction algorithms, such as PCA [2], LDA [2], LPP [3], MFA [10], DGPP [12], and the three latest multiview dimensionality reduction algorithms, including MVSE [18], MSNE [21], MSE [19], and SSMVE [22].
(7) MSNE [21]: MSNE is a probability-based unsupervised multiview algorithm.
(1) As can be seen from Tables 1, 2, and 3 and Figures 4, 5, and 6, our proposed MDGPP algorithm consistently outperforms the conventional single-view-based algorithms (i.e., PCA, LDA, LPP, MFA, and DGPP) and the latest multiview algorithms (i.e., MVSE, MSNE, MSE, and SSMVE) in all the experiments, which implies that extracting a discriminative feature subspace by using both intraclass geometry and interclass discrimination and explicitly considering the complementary information of different facial features can achieve the best recognition performance.
(2) The multiview learning algorithms (i.e., MVSE, MSNE, MSE, SSMVE, and MDGPP) perform much better than single-view-based algorithms (i.e., PCA, LDA, LPP, MFA, and DGPP), which demonstrates that simple concatenation strategy cannot duly combine features from multiple views, and the recognition performance can be successfully improved by exploring the complementary characteristics of different views.
(4) For the multiview learning algorithms, the supervised multiview algorithms (i.e., MSE and MDGPP) outperform the unsupervised multiview algorithms (i.e., MVSE, MSNE, and SSMVE) due to the utilization of the labeled facial images.
If [beta](A + [phi](1 - p))E/1 - p [less than or equal to] W [less than or equal to] [beta](A + [phi]p)E / p then at the MSNE the probability of a vote-buying candidate's win is [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], with which the probability of a vote-buying candidate's win is at a local minimum.
If pW/[beta](A + [phi]p) [less than or equal to] E [less than or equal to] (l - p)W/[beta](A + [phi](1 - p)), then at the MSNE the probability of a vote-buying candidate's win is [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], with which the probability of a vote-buying candidate's win is at a local minimum.
Merle Rosenzweig, AMLS (corresponding author), oriley@mich.edu, Liaison Librarian; Anna Ercoli Schnitzer, AMLS, schnitzr@umich.edu, Disabilities Librarian; Jean Song, MSI, jeansong@umich.edu, Research and Informatics Coordinator; Taubman Health Sciences Library, University of Michigan, 1135 East Catherine Street, 5726, Ann Arbor, MI 48109-2038; Scott Martin, MS, MSI, samarti@umich .edu, Biological Sciences Librarian, 3175 Shapiro Library, University of Michigan, 919 South University Avenue, Ann Arbor, MI 48109-1185; Jim Ottaviani, MSNE, MILS, Coordinator, Deep Blue, 8076-C Hatcher Library 1205, University of Michigan, Ann Arbor, MI 48109-1205