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160
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ML
2010
ACM
181views Machine Learning» more  ML 2010»
15 years 1 months ago
Decomposing the tensor kernel support vector machine for neuroscience data with structured labels
Abstract The tensor kernel has been used across the machine learning literature for a number of purposes and applications, due to its ability to incorporate samples from multiple s...
David R. Hardoon, John Shawe-Taylor
132
Voted
ICML
2006
IEEE
16 years 4 months ago
Simpler knowledge-based support vector machines
If appropriately used, prior knowledge can significantly improve the predictive accuracy of learning algorithms or reduce the amount of training data needed. In this paper we intr...
Quoc V. Le, Alex J. Smola, Thomas Gärtner
110
Voted
ICML
2005
IEEE
16 years 4 months ago
Supervised clustering with support vector machines
Supervised clustering is the problem of training a clustering algorithm to produce desirable clusterings: given sets of items and complete clusterings over these sets, we learn ho...
Thomas Finley, Thorsten Joachims
130
Voted
JMLR
2006
131views more  JMLR 2006»
15 years 3 months ago
Incremental Support Vector Learning: Analysis, Implementation and Applications
Incremental Support Vector Machines (SVM) are instrumental in practical applications of online learning. This work focuses on the design and analysis of efficient incremental SVM ...
Pavel Laskov, Christian Gehl, Stefan Krüger, ...
127
Voted
ISBI
2008
IEEE
16 years 4 months ago
Flexible synapse detection in fluorescence micrographs by modeling human expert grading
A particularly difficult task in molecular imaging is the analysis of fluorescence microscopy images of neural tissue, as they usually exhibit a high density of objects with diffu...
Julia Herold, Manuela Friedenberger, Marcus Bode, ...