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NIPS
1998
15 years 7 months ago
Coding Time-Varying Signals Using Sparse, Shift-Invariant Representations
A common way to represent a time series is to divide it into shortduration blocks, each of which is then represented by a set of basis functions. A limitation of this approach, ho...
Michael S. Lewicki, Terrence J. Sejnowski
IJAR
2006
89views more  IJAR 2006»
15 years 6 months ago
Learning probabilistic decision graphs
Probabilistic decision graphs (PDGs) are a representation language for probability distributions based on binary decision diagrams. PDGs can encode (context-specific) independence...
Manfred Jaeger, Jens D. Nielsen, Tomi Silander
AIMSA
2004
Springer
15 years 9 months ago
Advances in Profile Assisted Voicemail Management
Abstract. Spoken audio is an important source of information available to knowledge extraction and management systems. Organization of spoken messages by priority and content can f...
Konstantinos Koumpis
BIODATAMINING
2008
120views more  BIODATAMINING 2008»
15 years 6 months ago
Filling the gap between biology and computer science
This editorial introduces BioData Mining, a new journal which publishes research articles related to advances in computational methods and techniques for the extraction of useful ...
Jesús S. Aguilar-Ruiz, Jason H. Moore, Mary...
CVPR
2008
IEEE
16 years 8 months ago
Conditional density learning via regression with application to deformable shape segmentation
Many vision problems can be cast as optimizing the conditional probability density function p(C|I) where I is an image and C is a vector of model parameters describing the image. ...
Jingdan Zhang, Shaohua Kevin Zhou, Dorin Comaniciu...