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ICML
2000
IEEE
15 years 10 months ago
Maximum Entropy Markov Models for Information Extraction and Segmentation
Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech t...
Andrew McCallum, Dayne Freitag, Fernando C. N. Per...
CVPR
2008
IEEE
15 years 11 months ago
Spectrally optimal factorization of incomplete matrices
From the recovery of structure from motion to the separation of style and content, many problems in computer vision have been successfully approached by using bilinear models. The...
Pedro M. Q. Aguiar, João M. F. Xavier, Mark...
ICPR
2006
IEEE
15 years 10 months ago
Measurement Function Design for Visual Tracking Applications
Extracting human postural information from video sequences has proved a difficult research question. The most successful approaches to date have been based on particle filtering, ...
Andrew W. B. Smith, Brian C. Lovell
COMCOM
2010
179views more  COMCOM 2010»
14 years 9 months ago
On the statistical characterization of flows in Internet traffic with application to sampling
A new method of estimating some statistical characteristics of TCP flows in the Internet is developed in this paper. For this purpose, a new set of random variables (referred to as...
Yousra Chabchoub, Christine Fricker, Fabrice Guill...
ICRA
2009
IEEE
100views Robotics» more  ICRA 2009»
15 years 4 months ago
Theoretical analysis of three bio-inspired plume tracking algorithms
— We derive the theoretical performance of three bio-inspired odor source localization algorithms (casting, surgespiral and surge-cast) in laminar wind flow. Based on the geomet...
Thomas Lochmatter, Alcherio Martinoli