Results 241 to 250 of about 9,239,414 (293)
Some of the next articles are maybe not open access.
Principal feature classification
IEEE Transactions on Neural Networks, 1997The concept, structures, and algorithms of principal feature classification (PFC) are presented in this paper. PFC is intended to solve complex classification problems with large data sets. A PFC network is designed by sequentially finding principal features and removing training data which has already been correctly classified. PFC combines advantages
Q, Li, D W, Tufts
openaire +2 more sources
Principal-principal conflicts during crisis
Asia Pacific Journal of Management, 2010This paper explores principal-principal conflicts in corporate governance during times of economic crisis. We address the question: What external and internal governance mechanisms can best protect minority shareholders? Drawing on 877 publicly listed large corporations with concentrated ownership in seven Asian countries and regions, we compare ...
Jiang, Y., Peng, Mike
openaire +2 more sources
2008
Principal to Principal: Conversations in Servant Leadership and School Transformation takes the reader into the real world of school leadership, as a retiring elementary principal and his successor engage in a 12-month mentoring partnership. Packed with emotion and the complexities of the twenty-first century school, the story weaves in and out of the ...
openaire +1 more source
Principal to Principal: Conversations in Servant Leadership and School Transformation takes the reader into the real world of school leadership, as a retiring elementary principal and his successor engage in a 12-month mentoring partnership. Packed with emotion and the complexities of the twenty-first century school, the story weaves in and out of the ...
openaire +1 more source
2012
Principal components analysis (PCA) is a standard tool in multivariate data analysis to reduce the number of dimensions, while retaining as much as possible of the data's variation. Instead of investigating thousands of original variables, the first few components containing the majority of the data's variation are explored.
Groth, D. +3 more
openaire +3 more sources
Principal components analysis (PCA) is a standard tool in multivariate data analysis to reduce the number of dimensions, while retaining as much as possible of the data's variation. Instead of investigating thousands of original variables, the first few components containing the majority of the data's variation are explored.
Groth, D. +3 more
openaire +3 more sources
From Principal Curves to Granular Principal Curves
IEEE Transactions on Cybernetics, 2014Principal curves arising as an essential construct in dimensionality reduction and data analysis have recently attracted much attention from theoretical as well as practical perspective. In many real-world situations, however, the efficiency of existing principal curves algorithms is often arguable, in particular when dealing with massive data owing to
Hongyun, Zhang +3 more
openaire +2 more sources
Decrease of Principal-Principall Conflicts [PDF]
SSRN Electronic Journal, 2015 Most of analyses for corporate governance have a company with dispersed ownership as a research object. Relevant to this type of company classical conflict „principal-agent“ is decide by traditional mechanisms of corporate governance and mainly by internal mechanisms.
openaire +1 more source
2014
Long entry in "Encyclopedia of Quality of Life and Well-Being Research"
openaire +2 more sources
Long entry in "Encyclopedia of Quality of Life and Well-Being Research"
openaire +2 more sources
Invigorating principal/assistant principal relationships
Phi Delta KappanRelationships between and among school administrative team members are critical components of school culture. They serve as a guidepost for the behaviors and attitudes of staff and students. To invigorate these relationships, three embodied practices have high yield not only for the relationships among administrators, but also for fostering a healthy ...
openaire +1 more source
Principal components or principal axes
1999The expression principal components first appeared in the writings of the American statistician Harold Hotelling in 1933, but the technique was known earlier as principal axes and goes back to Karl Pearson (1901). In principle, principal components are appropriate for the analysis of variation within a sample coming from a single statistical population
openaire +1 more source