نتایج جستجو برای: re sampling

تعداد نتایج: 383714  

2015
Evandro Brasil da Fonseca Renata Vieira Aline A. Vanin

In this paper we present our proposed model for coreference resolution and we discuss the imbalanced dataset problem related to this task.We conduct a few experiments showing how well our set of features can solve coreference for Portuguese. In order to minimize the imbalance between the classes, we evalaluated the system on the basis of well known re-sampling techniques.

2010
Joel O. Wertheim

In 1977, H1N1 influenza A virus reappeared after a 20-year absence. Genetic analysis indicated that this strain was missing decades of nucleotide sequence evolution, suggesting an accidental release of a frozen laboratory strain into the general population. Recently, this strain and its descendants were included in an analysis attempting to date the origin of pandemic influenza virus without ac...

2002
Riadh Kallel Joseph Rynkiewicz

This work concernes the contrast difference test and its asymptotic properties for non linear auto-regressive models. Our approach is based on an application of the parametric bootstrap method. It is a re-sampling method based on the estimate parameters of the models. The resulting methodology is illustrated by simulations of multilayer perceptron models, and an asymptotic justification is give...

2015
Masashi Wada Mayumi Tsukada Norikazu Namiki Wladyslaw W. Szymanski Naoki Noda Hisao Makino Chikao Kanaoka Hidehiro Kamiya

Two ISO standard methods for in-stack sampling and measurement of PM2.5 and PM10 mass concentrations in flue gas from stationary sources were published in 2009 (ISO 23210, conventional cascade impactors) and 2012 (ISO 13271, virtual impactors). The performances of these two methods in terms of PM2.5 separation efficiency and the accuracy of measured mass concentration were compared at the same ...

2002
Fioralba Cakoni Houssem Haddar

In recent years there has been considerable interest in the inverse scattering problem for anisotropic medium, particularly in the case of acoustic waves and electromagnetic waves. Due to the lack of uniqueness in determining the constitutive parameters, traditional methods for solving the inverse scattering problems based on the use of weak scattering approximations or nonlinear optimization t...

2000
Derek R. Magee Roger D. Boyle

A system for the tracking and classification of livestock movements is presented. The combined ‘tracker-classifier’ scheme is based on a variant of Isard and Blakes ‘Condensation’ algorithm [6] known as ‘Re-sampling Condensation’ in which a second set of samples is taken from each image in the input sequence based on the results of the initial Condensation sampling. This is analogous to a singl...

2011
D R Magee R D Boyle

A system for the tracking and classification of livestock movements is presented. The combined ‘tracker-classifier’ scheme is based on a variant of Isard and Blakes ‘Condensation’ algorithm [6] known as ‘Re-sampling Condensation’ in which a second set of samples is taken from each image in the input sequence based on the results of the initial Condensation sampling. This is analogous to a singl...

Journal: :Image Vision Comput. 2002
Derek R. Magee Roger D. Boyle

A system for the tracking and classification of livestock movements is presented. The combined ‘tracker-classifier’ scheme is based on a variant of Isard and Blakes ‘Condensation’ algorithm [Int. J. Comput. Vision (1998) 5] known as ‘Re-sampling Condensation’ in which a second set of samples is taken from each image in the input sequence based on the results of the initial Condensation sampling...

2015
Apurva Sonak

Imbalanced data set, a problem often found in real world application, can cause seriously negative effect on classification performance of machine learning algorithms. There have been many attempts at dealing with classification of unbalanced data sets. To handle the problem of imbalanced data is to re balance them artificially by oversampling and/or under-sampling.

1992
David L. Donoho

We describe several \wavelet transforms" which characterize smoothness spaces and for which the coe cients are obtained by sampling rather than integration. We use them to re-interpret the empirical wavelet transform, i.e. the common practice of applying pyramid lters to samples of a function.

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