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» Margin based Active Learning for LVQ Networks
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AI
2009
Springer
14 years 2 months ago
Grid-Enabled Adaptive Metamodeling and Active Learning for Computer Based Design
Many complex, real world phenomena are difficult to study directly using controlled experiments. Instead, the use of computer simulations has become commonplace as a feasible alte...
Dirk Gorissen
TNN
2010
234views Management» more  TNN 2010»
13 years 2 months ago
Novel maximum-margin training algorithms for supervised neural networks
This paper proposes three novel training methods, two of them based on the back-propagation approach and a third one based on information theory for Multilayer Perceptron (MLP) bin...
Oswaldo Ludwig, Urbano Nunes
PERCOM
2004
ACM
14 years 7 months ago
Learning to Detect User Activity and Availability from a Variety of Sensor Data
Using a networked infrastructure of easily available sensors and context-processing components, we are developing applications for the support of workplace interactions. Notions o...
Dave Snowdon, Jean-Luc Meunier, Martin Mühlen...
IROS
2008
IEEE
141views Robotics» more  IROS 2008»
14 years 2 months ago
Active sensing based dynamical object feature extraction
— This paper presents a method to autonomously extract object features that describe their dynamics from active sensing experiences. The model is composed of a dynamics learning ...
Shun Nishide, Tetsuya Ogata, Ryunosuke Yokoya, Jun...
ECTEL
2009
Springer
14 years 2 months ago
A Comparison of Paper-Based and Online Annotations in the Workplace
While reading documents, people commonly make annotations: they underline or highlight text and write comments in the margin. Making annotations during reading activities has been ...
Ricardo Kawase, Eelco Herder, Wolfgang Nejdl