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AI
2004
Springer
15 years 6 months ago
A selective sampling approach to active feature selection
Feature selection, as a preprocessing step to machine learning, has been very effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and imp...
Huan Liu, Hiroshi Motoda, Lei Yu
ICAT
2003
IEEE
15 years 11 months ago
Distance Learning of Chang'an in an Immersive Environment
We have made a computer graphics model of Chang'an City when it was the capital of China during the Tang Dynasty (7-10th century). The real-time rendered images are projected...
Miho Kobayashi, Kei Utsugi, Masami Yamasaki, Haruo...
AIED
2009
Springer
16 years 1 months ago
Detecting the Learning Value of Items In a Randomized Problem Set
Researchers that make tutoring systems would like to know which pieces of educational content are most effective at promoting learning among their students. Randomized controlled e...
Zachary A. Pardos, Neil T. Heffernan
BIBM
2007
IEEE
104views Bioinformatics» more  BIBM 2007»
15 years 10 months ago
A Protocol to Detect Local Affinities Involved in Proteins Distant Interactions
The tridimensional structure of a protein is constrained or stabilized by some local interactions between distant residues of the protein, such as disulfide bonds, electrostatic i...
Christophe Nicolas Magnan, Cécile Capponi, ...
RSFDGRC
2005
Springer
190views Data Mining» more  RSFDGRC 2005»
15 years 12 months ago
Finding Rough Set Reducts with SAT
Abstract. Feature selection refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas such as machine...
Richard Jensen, Qiang Shen, Andrew Tuson