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» Learning Determinantal Point Processes
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NIPS
1998
13 years 10 months ago
Semi-Supervised Support Vector Machines
We introduce a semi-supervised support vector machine (S3 VM) method. Given a training set of labeled data and a working set of unlabeled data, S3 VM constructs a support vector m...
Kristin P. Bennett, Ayhan Demiriz
BMCBI
2007
178views more  BMCBI 2007»
13 years 8 months ago
SVM clustering
Background: Support Vector Machines (SVMs) provide a powerful method for classification (supervised learning). Use of SVMs for clustering (unsupervised learning) is now being cons...
Stephen Winters-Hilt, Sam Merat
ICASSP
2007
IEEE
14 years 3 months ago
Protein Fold Recognition using Residue-Based Alignments of Sequence and Secondary Structure
Protein structure prediction aims to determine the three-dimensional structure of proteins form their amino acid sequences. When a protein does not have similarity (homology) to a...
Zafer Aydin, Hakan Erdogan, Yucel Altunbasak
AMC
2007
92views more  AMC 2007»
13 years 9 months ago
An integrated framework for continuous assessment and improvement of manufacturing systems
This paper presents an integrated framework for assessment and ranking of manufacturing systems based on management and organizational performance indicators. The integrated appro...
Ali Azadeh, S. F. Ghaderi, Y. Partovi Miran, V. Eb...
CGF
2010
161views more  CGF 2010»
13 years 9 months ago
HyperMoVal: Interactive Visual Validation of Regression Models for Real-Time Simulation
During the development of car engines, regression models that are based on machine learning techniques are increasingly important for tasks which require a prediction of results i...
Harald Piringer, Wolfgang Berger, J. Krasser