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» Variable selection using neural-network models
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NIPS
2001
14 years 11 months ago
Unsupervised Learning of Human Motion Models
This paper presents an unsupervised learning algorithm that can derive the probabilistic dependence structure of parts of an object (a moving human body in our examples) automatic...
Yang Song, Luis Goncalves, Pietro Perona
ECSA
2010
Springer
14 years 7 months ago
Feature-Based Composition of Software Architectures
In Software Product Lines variability refers to the definition and utilization of differences between several products. Feature Diagrams (FD) are a well-known approach to express v...
Carlos Andres Parra, Anthony Cleve, Xavier Blanc, ...
BMCBI
2006
148views more  BMCBI 2006»
14 years 9 months ago
Predicting Class II MHC-Peptide binding: a kernel based approach using similarity scores
Background: Modelling the interaction between potentially antigenic peptides and Major Histocompatibility Complex (MHC) molecules is a key step in identifying potential T-cell epi...
Jesper Salomon, Darren R. Flower
INFOCOM
1996
IEEE
15 years 1 months ago
Integrated Control of Connection Admission, Flow Rate, and Bandwidth for ATM Based Networks
: We consider the combined control problem of connection admission, flow rate, and bandwidth allocation (capacity, service-rate) under nonstationary conditions. A fluid flow model ...
Andreas Pitsillides, Petros A. Ioannou, David Tipp...
CMOT
2000
94views more  CMOT 2000»
14 years 9 months ago
Performance of Organizational Design Models and Their Impact on Organization Learning
Theperformanceofvariousorganizationalstructuresisanessentialparameterinthereengineeringoforganizations, particularly in the current rapidly changing, competitive and information t...
Aris M. Ouksel, Ronald Vyhmeister