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» Learning from Multiple Annotators with Gaussian Processes
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JMLR
2010
172views more  JMLR 2010»
14 years 4 months ago
Modeling annotator expertise: Learning when everybody knows a bit of something
Supervised learning from multiple labeling sources is an increasingly important problem in machine learning and data mining. This paper develops a probabilistic approach to this p...
Yan Yan, Rómer Rosales, Glenn Fung, Mark W....
ECAI
2008
Springer
14 years 11 months ago
Extracting Semantic Annotations from Moodle Data
The purpose of this paper is to provide a solution which allows automatic reasoning processes over Moodle activities logs, in order to obtain user-personalized recommendations. Act...
Mihai Gabroveanu, Ion-Mircea Diaconescu
UAI
2008
14 years 11 months ago
Modelling local and global phenomena with sparse Gaussian processes
Much recent work has concerned sparse approximations to speed up the Gaussian process regression from the unfavorable O(n3 ) scaling in computational time to O(nm2 ). Thus far, wo...
Jarno Vanhatalo, Aki Vehtari
MICAI
2010
Springer
14 years 7 months ago
Automatic Image Annotation Using Multiple Grid Segmentation
Abstract. Automatic image annotation refers to the process of automatically labeling an image with a predefined set of keywords. Image annotation is an important step of content-ba...
Gerardo Arellano, Luis Enrique Sucar, Eduardo F. M...
ICASSP
2011
IEEE
14 years 1 months ago
A partial least squares framework for speaker recognition
Modern approaches to speaker recognition (verification) operate in a space of “supervectors” created via concatenation of the mean vectors of a Gaussian mixture model (GMM) a...
Balaji Vasan Srinivasan, Dmitry N. Zotkin, Ramani ...