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Naïve Bayes Classification of High-Resolution Aerial Imagery
2021
International Journal of Advanced Computer Science and Applications
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In Naïve Bayes classification, each class is enclosed in a Producer accuracy is a measure of the accuracy of a
region in spectral space where its discriminant function is particular ...
Fig. 7 shows the Naïve Bayes classified image using linear
discriminant analysis. ...
doi:10.14569/ijacsa.2021.0121120
fatcat:lif6qymadrb77ksouf7lj5cg74
Dependence in Classification of Aluminium Waste
2015
Journal of Physics, Conference Series
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Linear Disciminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) are methods of classification was employed a grouping rule base on discriminant score. ...
PCA as a technique to reduce the data dimension at first and Bayes' theorem with copula function as a technique to classification of aluminium waste image. ...
Specially, the results in table 3 based on posterior probability as a classification rule, while in table 4 based on discriminant score such as done in [15] that employed QDA (Quadratic Discriminant ...
doi:10.1088/1742-6596/622/1/012052
fatcat:xnvktwdc5za4diixx6yhicljcy
Dimensionality Reduction and Classification through PCA and LDA
2015
International Journal of Computer Applications
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In this paper, well known techniques of Dimensionality Reduction namely Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are studied. ...
Advance datasets with large number of observations present new challenges in data, mining, analysis and classification. ...
used in many classification applications. ...
doi:10.5120/21790-5104
fatcat:eiuqguxhvze2lcv2b7wyqtvyqy
RKHS Bayes Discriminant: A Subspace Constrained Nonlinear Feature Projection for Signal Detection
2009
IEEE Transactions on Neural Networks
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Results are compared with linear and kernel discriminant analysis, as well as classification algorithms such as support vector machine, AdaBoost and LogitBoost. ...
Noticing that the Bayes classifier is in fact a nonlinear projection of the feature vector to a single-dimensional statistic, in this paper, we develop a smooth nonlinear projection filter constrained ...
Hence, rather than the whole space, one can seek the Bayes discriminant function in the span of class conditional probability densities (in other words, one's discriminant function should be regularized ...
doi:10.1109/tnn.2009.2021473
pmid:19497813
fatcat:ox335vzyxrb65pxzt3jonfngdy
A Comparative Analysis of Machine Learning Algorithms Used For Training in Face Recognition
2020
International Journal of Advanced Trends in Computer Science and Engineering
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Despite several benefits it provides, the precision of facial recognition may be further increased in cases where the conditions for image processing are not so good and where the photographs of a face ...
Face recognition is very common in today's modern world, and is widely used for much surveillance, safety, retail, tourism, healthcare, and hospitality applications. ...
Classification of facial images using the desired outputs based on Linear Discriminate Analysis was addressed in [19] . ...
doi:10.30534/ijatcse/2020/67952020
fatcat:gk6fnnvptnbh7llwcvdt67exca
Improvement of Fingerprint Retrieval by a Statistical Classifier
2011
IEEE Transactions on Information Forensics and Security
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The topics of fingerprint classification, indexing, and retrieval have been studied extensively in the past decades. ...
Hence most of the previous works resorted to simple -nearest neighbor ( -NN) classification. However, the -NN classifier has the drawbacks of being comparatively slow and less accurate. ...
We implement a variant of the Fisher's linear discriminant for dimension reduction and a variant of the quadratic discriminant function (Bayes decision function assuming Gaussian statistics) for lowering ...
doi:10.1109/tifs.2010.2100382
fatcat:urahiesoe5g5pmmscvcz6j5vs4
Robust Adjusted Likelihood Function for Image Analysis
2006
IEEE Applied Imagery and Pattern Recognition Workshop
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f i (X,θ i ))} • The classification boundary is b + w l(x,θ 1 ) = l(x,θ 2 ), where b = {log(η 1 )-log(η 2 )}/ξ 2 and w =ξ 1 /ξ 2 , this classification boundary is in a form of a linear discriminant function ...
-If w=1 and b=0, it reduces to the Bayes classification rule in the data space • A major advantage of the RAL is that its classification rule includes the Bayes classification rule as a special case. ...
Study on Simulated Data
Application on SAR ATR
Conclusion • The RAL classification is robust in classification when model assumption is not correct. • Minimum error rate method is effective in estimating ...
doi:10.1109/aipr.2006.34
dblp:conf/aipr/DuanJM06
fatcat:saaimx4nwjcuvdzfwhleek4w6a
Crop identification and disease classification using traditional machine learning and deep learning approaches
2021
Maǧallaẗ al-abḥāṯ al-handasiyyaẗ
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Discriminant analysis, Naive Bayes algorithm, support vector machine and neural network were the classification algorithms used with a resulting best accuracy of 97.61%, 95.62%, 98.01% and 98.94% for crop ...
Similarly, application of algorithm with 6 histogram-based descriptors for health status detection resulted in an accuracy of 66.67%, 37.04%, 50% and 72.9% respectively. ...
Fisher's linear discriminant algorithm was used for classification of the disease. ...
doi:10.36909/jer.11941
fatcat:hyr3ia6amjh4hcw4peabgwz4ty
An Automated Technique using Gaussian Naïve Bayes Classifier to Classify Breast Cancer
2016
International Journal of Computer Applications
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Statistical analysis: Variable selection is done by one of the variable reduction algorithm called Linear Discriminant Analysis (LDA). LDA is one of the statistical method. ...
The dataset is passed to LDA function repeatedly and the combination of variables which gave the good accuracy is selected. ...
Linear Discriminant Analysis is done before applying dataset in the application. ...
doi:10.5120/ijca2016911146
fatcat:s6kgruisi5d7phirlbgweyni4q
Kernel-based Generative Learning in Distortion Feature Space
[article]
2016
arXiv
pre-print
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The recognition diversity indicates that a hybrid combination of the proposed generative classifier and the discriminative classifier could further improve the classification performance. ...
recognition capability compared to the state-of-the-art discriminative classifier - deep belief network. ...
widely used in machine learning as real-world applications. The MNIST data set of handwritten digits contains a training set of 60, 000 images, and a test set of 10, 000 images. ...
arXiv:1606.06377v1
fatcat:eshk2zt2yffxvjrerwjal22rka
Sparse Kernels for Bayes Optimal Discriminant Analysis
2007
2007 IEEE Conference on Computer Vision and Pattern Recognition
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Discriminant Analysis (DA) methods have demonstrated their utility in countless applications in computer vision and other areas of research -especially in the C class classification problem. ...
In this approach, we first (intrinsically) map the original nonlinear problem to a linear one and then use LDA to find the C − 1-dimensional Bayes optimal subspace. ...
In this application, we first extract a set of p features from the image -be it geometric or appearance features or a combination of them. ...
doi:10.1109/cvpr.2007.383409
dblp:conf/cvpr/HamsiciM07
fatcat:rtdnvl6a7bhr5dx32ezcrxwlsu
Is gender classification across ethnicity feasible using discriminant functions?
2011
2011 International Joint Conference on Biometrics (IJCB)
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This research aims at studying the performance of discriminant functions including Principal Component Analysis, Linear Discriminant Analysis and Subclass Discriminant Analysis with the availability of ...
The experiments are performed on a heterogeneous database of 8112 images that includes variations in illumination, expression, minor pose and ethnicity. ...
Acknowledgement This research is supported through a grant from the Department of Information Technology, India. ...
doi:10.1109/ijcb.2011.6117524
dblp:conf/icb/DhamechaSSV11
fatcat:e5dldc77fvbxnkhhva5dc3np3u
A Survey on Various Classification Techniques for Medical Image Data
2014
International Journal of Computer Applications
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In this paper a survey on various classification techniques for medical image and also its application for detection of many diseases. Several classification techniques are investigated till today. ...
In past many classification techniques by using GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) are commonly used. ...
It is important for many application of computer image analysis for classification of segmentation of image based on local spatial variations of intensity of color. ...
doi:10.5120/17080-7528
fatcat:pc5n7awlknbwpddaf6val5pssy
URBAN ENVIRONMENTAL QUALITY ASSESSMENT BY SHAPE AND SPECTRAL INDICES OF MULBERRY LEAVES
2019
Applied Researches in Technics, Technologies and Education
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It has been found that a kernel variant of the principal components, combined with nonlinear separating functions of discriminant analysis and a method of support vector machines, are an appropriate methods ...
Methods have been used to reduce the amount of data of latent variables, linear and kernel variants of principal components. ...
The following separation (discriminant) functions were used in the discriminant analysis: Linear (L) -linear separation function, suitable for multivariate normal density data of each group, with a total ...
doi:10.15547/artte.2019.03.004
fatcat:uoeq5tcuzfhh3gy4b36bkntbky
Integrative linear discriminant analysis with guaranteed error rate improvement
2018
Biometrika
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In the context of two-class classification, we propose an integrative linear discriminant analysis method and establish a theoretical guarantee that it achieves a smaller classification error than running ...
linear discriminant analysis on each data type individually. ...
National Science Foundation and National Institutes of Health. The authors thank Dr William Jagust and Dr Samuel Lockhart for helpful discussions on the positron emission tomography study. ...
doi:10.1093/biomet/asy047
pmid:31762476
pmcid:PMC6874859
fatcat:zvhv5ejkrnawlb4n73i566vejm
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