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ICASSP
2008
IEEE
15 years 3 months ago
Discriminative feature selection for hidden Markov models using Segmental Boosting
We address the feature selection problem for hidden Markov models (HMMs) in sequence classification. Temporal correlation in sequences often causes difficulty in applying featur...
Pei Yin, Irfan A. Essa, Thad Starner, James M. Reh...
PERCOM
2010
ACM
14 years 7 months ago
All for one or one for all? Combining heterogeneous features for activity spotting
Abstract—Choosing the right feature for motion based activity spotting is not a trivial task. Often, features derived by intuition or that proved to work well in previous work ar...
Ulf Blanke, Bernt Schiele, Matthias Kreil, Paul Lu...
ICMCS
2005
IEEE
128views Multimedia» more  ICMCS 2005»
15 years 3 months ago
Low-complexity automatic speaker recognition in the compressed GSM AMR domain
This paper presents an experimental implementation of a low-complexity speaker recognition algorithm working in the compressed speech domain. The goal is to perform speaker modeli...
Matteo Petracca, Antonio Servetti, Juan Carlos De ...
ICPR
2008
IEEE
15 years 3 months ago
Robust indoor activity recognition via boosting
In this paper, a novel statistical indoor activity recognition algorithm is introduced. While conditional random fields (CRFs) have prominent properties to this task, no optimal ...
Masamichi Shimosaka, Taketoshi Mori, Tomomasa Sato
ICTAI
1993
IEEE
15 years 1 months ago
Robust Feature Selection Algorithms
Selecting a set of features which is optimal for a given task is a problem which plays an important role in a wide variety of contexts including pattern recognition, adaptive cont...
Haleh Vafaie, Kenneth DeJong