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» Learning Object Representations Using Sequential Patterns
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ICA
2007
Springer
14 years 1 months ago
Discovering Convolutive Speech Phones Using Sparseness and Non-negativity
Discovering a representation that allows auditory data to be parsimoniously represented is useful for many machine learning and signal processing tasks. Such a representation can b...
Paul D. O'Grady, Barak A. Pearlmutter
ICPR
2006
IEEE
14 years 8 months ago
Dissimilarity-based classification for vectorial representations
General dissimilarity-based learning approaches have been proposed for dissimilarity data sets [11, 10]. They arise in problems in which direct comparisons of objects are made, e....
Elzbieta Pekalska, Robert P. W. Duin
BMCBI
2007
115views more  BMCBI 2007»
13 years 7 months ago
A novel, fast, HMM-with-Duration implementation - for application with a new, pattern recognition informed, nanopore detector
Background: Hidden Markov Models (HMMs) provide an excellent means for structure identification and feature extraction on stochastic sequential data. An HMM-with-Duration (HMMwD) ...
Stephen Winters-Hilt, Carl Baribault
ITCC
2003
IEEE
14 years 29 days ago
A Learning Objects Approach to Teaching Programming
The goal of this paper is to describe a new approach to a content creation and delivery mechanism for a programming course. This approach is based on the concept of creating a lar...
Victor Adamchik, Ananda Gunawardena
ACCV
2006
Springer
14 years 1 months ago
A Local Basis Representation for Estimating Human Pose from Cluttered Images
Recovering the pose of a person from single images is a challenging problem. This paper discusses a bottom-up approach that uses local image features to estimate human upper body p...
Ankur Agarwal, Bill Triggs