scholarly journals Bayesian discriminant analysis of plant leaf hyperspectral reflectance for identification of weeds from cabbages

2016 ◽  
Vol 11 (7) ◽  
pp. 551-562 ◽  
Author(s):  
Deng Wei ◽  
Huang Yanbo ◽  
Zhao Chunjiang ◽  
Chen Liping ◽  
Wang Xiu
Keyword(s):  
Discriminant Analysis ◽  
Hyperspectral Reflectance ◽  
Plant Leaf ◽  
Bayesian Discriminant Analysis

Bayesian discriminant analysis for prediction of coal and gas outbursts and applicatio

2010 ◽  
Vol 20 (4) ◽  
pp. 520-523 ◽  
Author(s):  
Chao WANG ◽  
Enyuan WANG ◽  
Jiankun XU ◽  
Xiaofei LIU ◽  
Li LING
Keyword(s):  
Discriminant Analysis ◽  
Coal And Gas Outbursts ◽  
Bayesian Discriminant Analysis

scholarly journals Predicting the Probability of Helper T Cell Immunodominant Sites through Discriminant Analysis

1993 ◽  
Vol 30 (2) ◽  
pp. 152-156 ◽  
Author(s):  
Ronna R Mallios
Keyword(s):  
T Cells ◽  
Amino Acid ◽  
T Cell ◽  
Discriminant Analysis ◽  
Predictive Validity ◽  
Helper T Cells ◽  
Predictor Variables ◽  
Amino Acid Residues ◽  
Modelling Strategies ◽  
Bayesian Discriminant Analysis

Bayesian discriminant analysis is used to predict whether or not a given protein segment will activate helper T cells. The predictor variables are drawn from the products of frequencies of amino acid residues. The model's predictive validity compares favourably with that of alternative modelling strategies, suggesting that this approach merits further investigation.

Hyperspectral reflectance imaging to classify lettuce varieties by optimum selected wavelengths and linear discriminant analysis

2020 ◽  
Vol 20 ◽  
pp. 100400
Author(s):  
Renato Herrig Furlanetto ◽  
Thaise Moriwaki ◽  
Renan Falcioni ◽  
Mariana Pattaro ◽  
Alessandra Vollmann ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Hyperspectral Reflectance ◽  
Linear Discriminant

scholarly journals A Novel Model Using Virtual State Variables and Bayesian Discriminant Analysis to Classify Surrounding Rock Stability

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jinglai Sun ◽  
Darui Ren ◽  
Yu Song ◽  
Mingyuan Yu ◽  
Zhaofei Chu ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Surrounding Rock ◽  
Virtual State ◽  
Test Method ◽  
Ratio Test ◽  
State Variables ◽  
Optimal Dimension ◽  
Rock Stability ◽  
Training Samples ◽  
Bayesian Discriminant Analysis

To accurately classify the stability of surrounding rock masses, a novel method (VSV-BDA) based on virtual state variables (VSVs) and Bayesian discriminant analysis (BDA) is proposed. The factors influencing stability are mapped by an artificial neural network (ANN) capable of recognizing the model of rock mass classification, and the obtained output vector is treated as VSVs, which are verified as obeying a multinormal distribution with equal covariance matrixes by normal distribution testing and constructed statistics. The prediction variance ratio test method is introduced to determine the optimal dimension of the VSVs. The VSV-BDA model is constructed through the use of VSVs and the optimal dimension on the basis of the training samples, which are divided from the collected samples into three situations having different numbers. ANN and BDA models are also constructed based on the same training samples. The predictions by the three models for the testing samples are compared; the results show that the proposed VSV-BDA model has high prediction accuracy and can be applied in practical engineering.

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