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» On the Learnability of Vector Spaces
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ISBI
2008
IEEE
14 years 9 months ago
Support vector machine for data on manifolds: An application to image analysis
The Support Vector Machine (SVM) is a powerful tool for classification. We generalize SVM to work with data objects that are naturally understood to be lying on curved manifolds, ...
Suman K. Sen, Mark Foskey, James Stephen Marron, M...
ICASSP
2011
IEEE
13 years 6 days ago
Learning and inference algorithms for partially observed structured switching vector autoregressive models
We present learning and inference algorithms for a versatile class of partially observed vector autoregressive (VAR) models for multivariate time-series data. VAR models can captu...
Balakrishnan Varadarajan, Sanjeev Khudanpur
ETS
2006
IEEE
119views Hardware» more  ETS 2006»
14 years 2 months ago
On-Chip Test Generation Using Linear Subspaces
A central problem in built-in self test (BIST) is how to efficiently generate a small set of test vectors that detect all targeted faults. We propose a novel solution that uses l...
Ramashis Das, Igor L. Markov, John P. Hayes
EOR
2007
101views more  EOR 2007»
13 years 8 months ago
Comprehensible credit scoring models using rule extraction from support vector machines
In recent years, Support Vector Machines (SVMs) were successfully applied to a wide range of applications. Their good performance is achieved by an implicit non-linear transformat...
David Martens, Bart Baesens, Tony Van Gestel, Jan ...
ICMCS
2006
IEEE
112views Multimedia» more  ICMCS 2006»
14 years 2 months ago
Visual Feature Space Analysis for Unsupervised Effectiveness Estimation and Feature Engineering
The Feature Vector approach is one of the most popular schemes for managing multimedia data. For many data types such as audio, images, or 3D models, an abundance of different Fea...
Tobias Schreck, Daniel A. Keim, Christian Panse