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103
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ITICSE
2006
ACM
15 years 9 months ago
ASSISTing CS1 students to learn: learning approaches and object-oriented programming
Identifying and managing students who experience difficulty with learning to program is a challenge for CS educators. In an attempt to answer the familiar question “What makes f...
Janet Hughes, D. Ramanee Peiris
145
Voted
BMCBI
2010
118views more  BMCBI 2010»
15 years 3 months ago
From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification
Background: Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limit...
Bram Slabbinck, Willem Waegeman, Peter Dawyndt, Pa...
CVPR
2008
IEEE
16 years 5 months ago
Boosting ordinal features for accurate and fast iris recognition
In this paper, we present a novel iris recognition method based on learned ordinal features.Firstly, taking full advantages of the properties of iris textures, a new iris represen...
Zhaofeng He, Zhenan Sun, Tieniu Tan, Xianchao Qiu,...
KDD
2001
ACM
211views Data Mining» more  KDD 2001»
16 years 4 months ago
Magical thinking in data mining: lessons from CoIL challenge 2000
CoIL challenge 2000 was a supervised learning contest that attracted 43 entries. The authors of 29 entries later wrote explanations of their work. This paper discusses these repor...
Charles Elkan
150
Voted
ICDM
2003
IEEE
220views Data Mining» more  ICDM 2003»
15 years 9 months ago
Exploiting Unlabeled Data for Improving Accuracy of Predictive Data Mining
Predictive data mining typically relies on labeled data without exploiting a much larger amount of available unlabeled data. The goal of this paper is to show that using unlabeled...
Kang Peng, Slobodan Vucetic, Bo Han, Hongbo Xie, Z...