Results 251 to 260 of about 3,902,266 (287)

Sparsity based face modelling and detection with small sample problem

2014 Twentieth National Conference on Communications (NCC), 2014
Face detection is an important and challenging task in many computer vision applications. Signal processing using sparse framework has seen much interest in various areas in the recent past. In this paper, we propose a sparse framework based methodology to model a human face using very few training faces.
Raju Ranjan, Sumana Gupta, K S Venkatesh
openaire   +1 more source

Decapod Crustacean Paleobiogeography: Resolving the Problem of Small Sample Size

Short Courses in Paleontology, 1990
Studies of paleobiogeography have changed markedly in recent decades transforming a once static subject into one which now has great potential as a useful counterpart to systematic and ecological studies in the interpretation of the geological history of organisms.
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The small sample size problem in gene expression tasks

2015
Charles University in Prague Faculty of Pharmacy in Hradec Králové Department of Biophysics and Physical Chemistry Candidate: Savvas Athanasiadis Supervisor: Jurjen Duintjer Tebbens Title of diploma thesis: The small sample size problem in gene expression tasks The thesis addresses classification of genes to tumor types based on their gene expression ...
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Reference range determination: the problem of small sample sizes.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 1992
The process of developing and validating a quantitative test includes determination of a reference range. Traditionally this has been taken as the mean +/- 2 standard deviations for a random sampling from a reference population. However, this method fails to recognize the substantial variability in the sample mean and standard deviation for the small ...
W D, Leslie, I D, Greenberg
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Subspace Regularized Linear Discriminant Analysis for Small Sample Size Problems

2012
Linear discriminant analysis (LDA) can extract features that preserve class separability. For small sample size (SSS) problems, the number of data samples is smaller than the dimension of data space, and the within-class scatter matrix of data samples is singular.
Zhidong Wang, Wuyi Yang
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Fast Calculation for Fisher Criteria in Small Sample Size Problem

2004
LDA is popularly used in the pattern recognition field Unfortunately LDA always confronts the small sample size problem (S3), which leads the within-class scatter matrix to be singular In this case, PCA is always used for dimensional reduction to solve the problem in practice This paper analyzes that when the small sample size problem happens, the PCA ...
WeiShi Zheng, JianHuang Lai, P. C. Yuen
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Small sample size problems in designing artificial neural networks

1991
Abstract Small training sample effects common in Statistical Classification and artificial neural network classifier design are discussed. A review of known small sample results are presented, and peaking phenomena related to the increase in the number of features and the number of neurons is discussed.
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Navigating financial toxicity in patients with cancer: A multidisciplinary management approach

Ca-A Cancer Journal for Clinicians, 2022
Grace Li Smith, Matthew P Banegas, Chiara Acquati, Lmsw   +2 more
exaly  

Regularized Complete Linear Discriminant Analysis for Small Sample Size Problems

2012
In small sample size (SSS) problems, the number of available training samples is smaller than the dimensionality of the sample space. Since linear discriminant analysis (LDA) requires the within-class scatter matrix to be non-sigular, LDA cannot be directly applied to SSS problems. In this paper, regularized complete linear discriminant analysis (RCLDA)
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WEIGHTED PROJECTION APPROACH FOR SMALL SAMPLE SIZE PROBLEM

Proceedings of the 11th Joint International Computer Conference, 2005
Wenan CHEN, Hongbin ZHANG
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