FN Archimer Export Format
PT J
TI Object recognition using proportion-based prior information Application to fisheries acoustics
BT 
AF LEFORT, Riwal
   FABLET, Ronan
   BOUCHER, I-M
AS 1:1,2;2:2;3:2;
FF 1:PDG-DOP-DCB-STH-LTH;2:;3:;
C1 IFREMER, STH French Res Inst Exploitat Sea Technople Brest, F-29280 Plouzane, France.
   Univ Europenne Bretagne Technople Brest Iroise, Lab Sticc, CNRS, Inst Telecom Telecom Bretagne,UMR, F-29238 Brest, France.
C2 IFREMER, FRANCE
   UEB, FRANCE
   TELECOM BRETAGNE, FRANCE
SI BREST
SE PDG-DOP-DCB-STH-LTH
IN WOS Ifremer jusqu'en 2018
   copubli-france
   copubli-univ-france
IF 1.034
TC 4
UR https://archimer.ifremer.fr/doc/00030/14103/11372.pdf
LA English
DT Article
DE ;Weakly supervised learning;Generative classification model;Discriminative classification model
AB This paper addresses the inference of probabilistic classification models using weakly supervised learning The main contribution of this work is the development of learning methods for training datasets consisting of groups of objects with known relative class priors This can be regarded as a generalization of the situation addressed by Bishop and Ulusoy (2005) where training information is given as the presence or absence of object classes in each set Generative and discriminative classification methods are conceived and compared for weakly supervised learning as well as a non-linear version of the probabilistic discriminative models The considered models are evaluated on standard datasets and an application to fisheries acoustics is reported The proposed proportion-based training is demonstrated to outperform model learning based on presence/absence information and the potential of the non-linear discriminative model is shown (C) 2010 Elsevier B V All rights reserved.
PY 2011
PD JAN
SO Pattern Recognition Letters
SN 0167-8655
PU Elsevier Science Bv
VL 32
IS 2
UT 000285703800008
BP 153
EP 158
DI 10.1016/j.patrec.2010.10.001
ID 14103
ER

EF