Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Large-Margin Methods"'
Autor:
Zhang, Tong
Publikováno v:
The Annals of Statistics, 2004 Feb 01. 32(1), 56-85.
Externí odkaz:
https://www.jstor.org/stable/3448494
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence
In this paper, we propose a maximum margin classifier that deals with uncertainty in data input. More specifically, we reformulate the SVM framework such that each training example can be modeled by a multi-dimensional Gaussian distribution described
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d3ae7b68cc2d438c618be364f96b4904
https://doi.org/10.1109/tpami.2017.2772235
https://doi.org/10.1109/tpami.2017.2772235
Autor:
Do, Quoc khanh
Durant ces dernières années, les architectures de réseaux de neurones (RN) ont été appliquées avec succès à de nombreuses applications en Traitement Automatique de Langues (TAL), comme par exemple en Reconnaissance Automatique de la Parole (R
Externí odkaz:
http://www.theses.fr/2016SACLS071/document
Publikováno v:
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2012, 60 (2), pp.648-659. ⟨10.1109/TSP.2011.2173685⟩
IEEE Transactions on Signal Processing, 60(2), 648-659
IEEE Transactions on Signal Processing 60 (2012) 2
IEEE TRANSACTIONS ON SIGNAL PROCESSING
IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2012, 60 (2), pp.648-659. ⟨10.1109/TSP.2011.2173685⟩
IEEE Transactions on Signal Processing, 60(2), 648-659
IEEE Transactions on Signal Processing 60 (2012) 2
IEEE TRANSACTIONS ON SIGNAL PROCESSING
International audience; Many signal processing problems are tackled by filtering the signal for subsequent feature classification or regression. Both steps are critical and need to be designed carefully to deal with the particular statistical chara
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b79286969ed33dac848a764b1d2c7c6
https://hal.archives-ouvertes.fr/hal-00528917v2/file/LM_filtering.pdf
https://hal.archives-ouvertes.fr/hal-00528917v2/file/LM_filtering.pdf
Akademický článek
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Publikováno v:
AAMAS 2014 : 13th International Conference on Autonomous Agents and Multiagent Systems
AAMAS 2014 : 13th International Conference on Autonomous Agents and Multiagent Systems, May 2014, Paris, France. pp.1249-1256
AAMAS 2014 : 13th International Conference on Autonomous Agents and Multiagent Systems, May 2014, Paris, France. pp.1249-1256
International audience; This paper deals with the problem of learning from demonstrations, where an agent called the apprentice tries to learn a behavior from demonstrations of another agent called the expert. To address this problem, we place oursel
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::20a7397d1d57f040caa704119b2ff8d6
https://inria.hal.science/hal-01107837
https://inria.hal.science/hal-01107837
Publikováno v:
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 20(5)
The authors examine the problem of segmenting foreground objects in live video when background scene textures change over time. In particular, we formulate background subtraction as minimizing a penalized instantaneous risk functional - yielding a lo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0334798388cbe4c2d0f2183a2cc536f4
https://pubmed.ncbi.nlm.nih.gov/20959270
https://pubmed.ncbi.nlm.nih.gov/20959270
Autor:
Tong Zhang
Publikováno v:
Ann. Statist. 32, no. 1 (2004), 56-85
We study how closely the optimal Bayes error rate can be approximately reached using a classification algorithm that computes a classifier by minimizing a convex upper bound of the classification error function. The measurement of closeness is charac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e697b439461f5b76ee63943fcf91a4b2
http://projecteuclid.org/euclid.aos/1079120130
http://projecteuclid.org/euclid.aos/1079120130
Akademický článek
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