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Journal articles on the topic 'Kernel discrimination'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 February 2022

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1

Araus, J. L., T. Amaro, J. Casadesús, A. Asbati, and M. M. Nachit. "Relationships between ash content, carbon isotope discrimination and yield in durum wheat." Functional Plant Biology 25, no. 7 (1998): 835. http://dx.doi.org/10.1071/pp98071.

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The relationships between ash content, carbon isotope discrimination and yield were studied in durum wheat (Triticum durum Desf.) grown in a Mediterranean region (north-western Syria) under three different water regimes (hereafter referred to as environments). Ash content (on dry mass basis) was measured in the flag leaf about 3 weeks after anthesis (leaf ash) and in mature kernels (kernel ash), whereas Δ was analysed in the penultimate leaf at heading (leaf Δ) and in mature kernels (kernel Δ). Leaf Δ was weakly or not related with the other parameters. Leaf ash correlated positively with kern

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Bian, Lu Sha, Yong Fang Yao, Xiao Yuan Jing, Sheng Li, Jiang Yue Man, and Jie Sun. "Face Recognition Based on a Fast Kernel Discriminant Analysis Approach." Advanced Materials Research 433-440 (January 2012): 6205–11. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.6205.

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The computational cost of kernel discrimination is usually higher than linear discrimination, making many kernel methods impractically slow. To overcome this disadvantage, several accelerated algorithms have been presented, which express kernel discriminant vectors using a part of mapped training samples that are selected by some criterions. However, they still need to calculate a large kernel matrix using all training samples, so they only save rather limited computing time. In this paper, we propose the fast and effective kernel discriminations based on the mapped mean samples (MMS). It calc

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SAKAKIBARA, YASUBUMI, KRIS POPENDORF, NANA OGAWA, KIYOSHI ASAI, and KENGO SATO. "STEM KERNELS FOR RNA SEQUENCE ANALYSES." Journal of Bioinformatics and Computational Biology 05, no. 05 (2007): 1103–22. http://dx.doi.org/10.1142/s0219720007003028.

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Several computational methods based on stochastic context-free grammars have been developed for modeling and analyzing functional RNA sequences. These grammatical methods have succeeded in modeling typical secondary structures of RNA, and are used for structural alignment of RNA sequences. However, such stochastic models cannot sufficiently discriminate member sequences of an RNA family from nonmembers and hence detect noncoding RNA regions from genome sequences. A novel kernel function, stem kernel, for the discrimination and detection of functional RNA sequences using support vector machines

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Jirsa, Ondřej, and Ivana Polišenská. "Identification of Fusarium damaged wheat kernels using image analysis." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 59, no. 5 (2011): 125–30. http://dx.doi.org/10.11118/actaun201159050125.

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Visual evaluation of kernels damaged by Fusarium spp. pathogens is labour intensive and due to a subjective approach, it can lead to inconsistencies. Digital imaging technology combined with appropriate statistical methods can provide much faster and more accurate evaluation of the visually scabby kernels proportion. The aim of the present study was to develop a discrimination model to identify wheat kernels infected by Fusarium spp. using digital image analysis and statistical methods. Winter wheat kernels from field experiments were evaluated visually as healthy or damaged. Deoxynivalenol (D

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Troshchynska, Yana, Roman Bleha, Lenka Kumbarová, Marcela Sluková, Andrej Sinica, and Jiří Štětina. "Characterisation of flaxseed cultivars based on NIR diffusion reflectance spectra of whole seeds and derived samples." Czech Journal of Food Sciences 37, No. 5 (2019): 374–82. http://dx.doi.org/10.17221/270/2018-cjfs.

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Discrimination of yellow and brown flaxseed cultivars was made based on diffusion reflectance FT-NIR spectra of whole seeds. The spectra of flaxseed kernels, hulls, defatted flours, and oils were also measured for comparison. Hierarchy cluster analysis (HCA) and principal component analysis (PCA) were used for the discrimination. Multivariate analyses of FT-NIR spectra led to satisfactory discrimination of all flaxseed cultivars of this study mainly according to the nutritionally important fatty acid composition that was confirmed by comparison with the corresponding spectra of flaxseed kernel

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El-Sebai, Osama A., Robert Sanderson, Max P. Bleiweiss, and Naomi Schmidt. "Detection of Sitotroga cerealella (Olivier) infestation of Wheat Kernels Using Hyperspectral Reflectance." Journal of Entomological Science 41, no. 2 (2006): 155–64. http://dx.doi.org/10.18474/0749-8004-41.2.155.

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Hyperspectral reflectance data were used to detect internal infestations of Angoumois grain moth, Sitotroga ceralella (Olivier), in wheat kernels. Kernel reflectance was measured with a spectroradiometer over a wavelength range of 350–2500 nm. Kernel samples were selected randomly and scanned every 7 d after infestation to determine the ability of the hyperspectral reflectance data to discriminate between infested and uninfested kernels. Immature stages of S. ceralella inside wheat kernels can be detected through changes in moisture, starch, and chitin content of the kernel. By using the spect

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SONG, HAN, FENG LI, PEIWEN GUANG, XINHAO YANG, HUANYU PAN, and FURONG HUANG. "Detection of Aflatoxin B1 in Peanut Oil Using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy Combined with Partial Least Squares Discriminant Analysis and Support Vector Machine Models." Journal of Food Protection 84, no. 8 (2021): 1315–20. http://dx.doi.org/10.4315/jfp-20-447.

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ABSTRACT This study was conducted to establish a rapid and accurate method for identifying aflatoxin contamination in peanut oil. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy combined with either partial least squares discriminant analysis (PLS-DA) or a support vector machine (SVM) algorithm were used to construct discriminative models for distinguishing between uncontaminated and aflatoxin-contaminated peanut oil. Peanut oil samples containing various concentrations of aflatoxin B1 were examined with an ATR-FTIR spectrometer. Preprocessed spectral data were i

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8

Chen, Beining, Robert F. Harrison, Jérôme Hert, Chido Mpanhanga, Peter Willett, and David J. Wilton. "Ligand-based virtual screening using binary kernel discrimination." Molecular Simulation 31, no. 8 (2005): 597–604. http://dx.doi.org/10.1080/08927020500134177.

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9

Ćwiklińska-Jurkowska, Małgorzata M. "Visualization and Comparison of Single and Combined Parametric and Nonparametric Discriminant Methods for Leukemia Type Recognition Based on Gene Expression." Studies in Logic, Grammar and Rhetoric 43, no. 1 (2015): 73–99. http://dx.doi.org/10.1515/slgr-2015-0043.

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Abstract A gene expression data set, containing 3051 genes and 38 tumor mRNA training samples, from a leukemia microarray study, was used for differentiation between ALL and AML groups of leukemia. In this paper, single and combined discriminant methods were applied on the basis of the selected few most discriminative variables according to Wilks’ lambda or the leave-one-out error of first nearest neighbor classifier. For the linear, quadratic, regularized, uncorrelated discrimination, kernel, nearest neighbor and naive Bayesian classifiers, two-dimensional graphs of the boundaries and discrim

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10

Chao, Guoqing, and Shiliang Sun. "Multi-kernel maximum entropy discrimination for multi-view learning." Intelligent Data Analysis 20, no. 3 (2016): 481–93. http://dx.doi.org/10.3233/ida-160816.

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11

Willett, Peter, David Wilton, Basil Hartzoulakis, Raymond Tang, John Ford, and David Madge. "Prediction of Ion Channel Activity Using Binary Kernel Discrimination." Journal of Chemical Information and Modeling 47, no. 5 (2007): 1961–66. http://dx.doi.org/10.1021/ci700087v.

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12

Tutz, Gerhard. "On cross-validation for discrete kernel estimates in discrimination." Communications in Statistics - Theory and Methods 18, no. 11 (1989): 4145–62. http://dx.doi.org/10.1080/03610928908830147.

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13

Koch, Inge, Kanta Naito, and Hiroaki Tanaka. "Kernel naive Bayes discrimination for high‐dimensional pattern recognition." Australian & New Zealand Journal of Statistics 61, no. 4 (2019): 401–28. http://dx.doi.org/10.1111/anzs.12279.

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14

Cao, Yice, Yan Wu, Ming Li, Wenkai Liang, and Peng Zhang. "PolSAR Image Classification Using a Superpixel-Based Composite Kernel and Elastic Net." Remote Sensing 13, no. 3 (2021): 380. http://dx.doi.org/10.3390/rs13030380.

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The presence of speckles and the absence of discriminative features make it difficult for the pixel-level polarimetric synthetic aperture radar (PolSAR) image classification to achieve more accurate and coherent interpretation results, especially in the case of limited available training samples. To this end, this paper presents a composite kernel-based elastic net classifier (CK-ENC) for better PolSAR image classification. First, based on superpixel segmentation of different scales, three types of features are extracted to consider more discriminative information, thereby effectively suppress

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15

Ghaedian, Ahmad R., and Randy L. Wehling. "Discrimination of Sound and Granary-Weevil-Larva-Infested Wheat Kernels by Near-Infrared Diffuse Reflectance Spectroscopy." Journal of AOAC INTERNATIONAL 80, no. 5 (1997): 997–1005. http://dx.doi.org/10.1093/jaoac/80.5.997.

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Abstract Sound and infested wheat kernels containing lateinstar granary weevil larvae, as identified by X-ray analysis, were used to evaluate the ability of nearinfrared (NIR) spectroscopy to predict the presence of insect larvae in individual wheat kernels. Diffuse reflectance spectra at 1100-2500 nm were recorded from individual infested and sound kernels. Principal component analysis (PCA) of NIR spectra from sound kernels was used to construct calibration models by calculation of Mahalanobis distances. Calibration models were then applied to spectra obtainedfrom both sound and infested ker

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Galindo-Noreña, Steven, David Cárdenas-Peña, and Álvaro Orozco-Gutierrez. "Multiple Kernel Stein Spatial Patterns for the Multiclass Discrimination of Motor Imagery Tasks." Applied Sciences 10, no. 23 (2020): 8628. http://dx.doi.org/10.3390/app10238628.

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Brain–computer interface (BCI) systems communicate the human brain and computers by converting electrical activity into commands to use external devices. Such kind of system has become an alternative for interaction with the environment for people suffering from motor disabilities through the motor imagery (MI) paradigm. Despite being the most widespread, electroencephalography (EEG)-based MI systems are highly sensitive to noise and artifacts. Further, spatially close brain activity sources and variability among subjects hampers the system performance. This work proposes a methodology for the

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Wilton, David J., Robert F. Harrison, Peter Willett, John Delaney, Kevin Lawson, and Graham Mullier. "Virtual Screening Using Binary Kernel Discrimination: Analysis of Pesticide Data." Journal of Chemical Information and Modeling 46, no. 2 (2006): 471–77. http://dx.doi.org/10.1021/ci050397w.

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18

Beiji, Zou, Nurudeen Mohammed, Zhu Chengzhang, Wang Lei, and Zhao Rongchang. "Overall Gabor Classifier (OGC) with Kernel Partial Least Square Discrimination." Journal of Computational and Theoretical Nanoscience 14, no. 8 (2017): 3727–36. http://dx.doi.org/10.1166/jctn.2017.6665.

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19

Wang, Boxiang, and Hui Zou. "A Multicategory Kernel Distance Weighted Discrimination Method for Multiclass Classification." Technometrics 61, no. 3 (2019): 396–408. http://dx.doi.org/10.1080/00401706.2018.1529629.

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20

Harrison, Robert F., and Kitsuchart Pasupa. "A simple iterative algorithm for parsimonious binary kernel Fisher discrimination." Pattern Analysis and Applications 13, no. 1 (2009): 15–22. http://dx.doi.org/10.1007/s10044-009-0162-1.

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21

Ma, Ying, Ke Qin, and Shunzhi Zhu. "Discrimination Analysis for Predicting Defect-Prone Software Modules." Journal of Applied Mathematics 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/675368.

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Software defect prediction studies usually build models without analyzing the data used in the procedure. As a result, the same approach has different performances on different data sets. In this paper, we introduce discrimination analysis for providing a good method to give insight into the inherent property of the software data. Based on the analysis, we find that the data sets used in this field have nonlinearly separable and class-imbalanced problems. Unlike the prior works, we try to exploit the kernel method to nonlinearly map the data into a high-dimensional feature space. By combating

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Štruc, Vitomir, and Nikola Pavešić. "Gabor-Based Kernel Partial-Least-Squares Discrimination Features for Face Recognition." Informatica 20, no. 1 (2009): 115–38. http://dx.doi.org/10.15388/informatica.2009.240.

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23

Ahn, Jeongyoun. "A stable hyperparameter selection for the Gaussian RBF kernel for discrimination." Statistical Analysis and Data Mining: The ASA Data Science Journal 3, no. 3 (2010): 142–48. http://dx.doi.org/10.1002/sam.10073.

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24

Min, Beomjun, Jongin Kim, Hyeong-jun Park, and Boreom Lee. "Vowel Imagery Decoding toward Silent Speech BCI Using Extreme Learning Machine with Electroencephalogram." BioMed Research International 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/2618265.

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The purpose of this study is to classify EEG data on imagined speech in a single trial. We recorded EEG data while five subjects imagined different vowels, /a/, /e/, /i/, /o/, and /u/. We divided each single trial dataset into thirty segments and extracted features (mean, variance, standard deviation, and skewness) from all segments. To reduce the dimension of the feature vector, we applied a feature selection algorithm based on the sparse regression model. These features were classified using a support vector machine with a radial basis function kernel, an extreme learning machine, and two va

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Nomura, Masaki, Yoshio Sakurai, and Toshio Aoyagi. "Analysis of Multineuron Activity Using the Kernel Method." Journal of Robotics and Mechatronics 19, no. 4 (2007): 364–68. http://dx.doi.org/10.20965/jrm.2007.p0364.

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We recorded multineuron spike time-series data from rat hippocampus region CA1 during a conditional discrimination task. We separated out individual single-neuron activity from multineuron activity data and prepared spike count data and calculated a kernel matrix using a Spikernel function, then applied k-means clustering and principal component analysis (PCA). Comparing spike count data to an appropriate time, we divided data into clusters and found the correspondence between the obtained cluster and rat activity. We discuss information expression in nervous-system activity expected from the

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Fan, Zhong Jie, Yan Qiu Leng, Yong Long Xu, Zheng Jiang Meng, and Ji Wei Xu. "A Discrimination Method of Saturated Sand Liquefaction Possibility Based on Support Vector Machine." Applied Mechanics and Materials 509 (February 2014): 38–43. http://dx.doi.org/10.4028/www.scientific.net/amm.509.38.

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Based on the analysis of influence factors of saturated sand, this paper expounds the limitations of traditional evaluation of liquefaction, and introduces the criterion of support vector machine (SVM) based on the principle of structural risk minimization. According to the main influence factors of sand liquefaction, a SVM discriminant model of sand liquefaction with different kernel functions is established. Through studying small sample data, this model can establish nonlinear mapping relationship between influence factors and liquefaction type. On the basis of seismic data, a radial based

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Royo, C., D. Villegas, L. F. García del Moral, et al. "Comparative performance of carbon isotope discrimination and canopy temperature depression as predictors of genotype differences in durum wheat yield in Spain." Australian Journal of Agricultural Research 53, no. 5 (2002): 561. http://dx.doi.org/10.1071/ar01016.

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The relationships between carbon isotope discrimination (Δ) in mature kernels, canopy temperature depression (CTD) during anthesis and grain filling, 1000-kernel weight (TKW), total carbon content of mature kernels, and yield were studied in durum wheat (Triticum turgidum L. var. durum) grown in Spain (western Mediterranean basin). Twenty-five durum wheat genotypes were grown in 2 regions (NE and SE Spain) and under 2 water regimes (rainfed v. support irrigation) from 1997 to 1999 (i.e. a total of 12 trials). Principal component analysis placed yield and Δ on the same axis. Pearson’s correlati

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SCHLEIF, F. M., THOMAS VILLMANN, BARBARA HAMMER, and PETRA SCHNEIDER. "EFFICIENT KERNELIZED PROTOTYPE BASED CLASSIFICATION." International Journal of Neural Systems 21, no. 06 (2011): 443–57. http://dx.doi.org/10.1142/s012906571100295x.

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Prototype based classifiers are effective algorithms in modeling classification problems and have been applied in multiple domains. While many supervised learning algorithms have been successfully extended to kernels to improve the discrimination power by means of the kernel concept, prototype based classifiers are typically still used with Euclidean distance measures. Kernelized variants of prototype based classifiers are currently too complex to be applied for larger data sets. Here we propose an extension of Kernelized Generalized Learning Vector Quantization (KGLVQ) employing a sparsity an

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Harper, Gavin, John Bradshaw, John C. Gittins, Darren V. S. Green, and Andrew R. Leach. "Prediction of Biological Activity for High-Throughput Screening Using Binary Kernel Discrimination." Journal of Chemical Information and Computer Sciences 41, no. 5 (2001): 1295–300. http://dx.doi.org/10.1021/ci000397q.

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Pilario, Karl Ezra, Alexander Tielemans, and Elmer-Rico E. Mojica. "Geographical discrimination of propolis using dynamic time warping kernel principal components analysis." Expert Systems with Applications 187 (January 2022): 115938. http://dx.doi.org/10.1016/j.eswa.2021.115938.

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Fazzini, Paolo, Giuseppina De Felice Proia, Maria Adamo, et al. "Sentinel-2 Remote Sensed Image Classification with Patchwise Trained ConvNets for Grassland Habitat Discrimination." Remote Sensing 13, no. 12 (2021): 2276. http://dx.doi.org/10.3390/rs13122276.

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The present study focuses on the use of Convolutional Neural Networks (CNN or ConvNet) to classify a multi-seasonal dataset of Sentinel-2 images to discriminate four grassland habitats in the “Murgia Alta” protected site. To this end, we compared two approaches differing only by the first layer machinery, which, in one case, is instantiated as a fully-connected layer and, in the other case, results in a ConvNet equipped with kernels covering the whole input (wide-kernel ConvNet). A patchwise approach, tessellating training reference data in square patches, was adopted. Besides assessing the ef

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Bourouhou, Abdelhamid, Abdelilah Jilbab, Chafik Nacir, and Ahmed Hammouch. "Heart Sounds Classification for a Medical Diagnostic Assistance." International Journal of Online and Biomedical Engineering (iJOE) 15, no. 11 (2019): 88. http://dx.doi.org/10.3991/ijoe.v15i11.10804.

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<span lang="EN-US">In order to develop the assessment of phonocardiogram “PCG” signal for discrimination between two of people classes – individuals with heart disease and healthy one- we have adopted the database provided by "The PhysioNet/Computing in Cardilogy Challenge 2016", which contains records of heart sounds 'PCG '. This database is chosen in order to compare and validate our results with those already published. We subsequently extracted 20 features from each provided record. For classification, we used the Generalized Linear Model (GLM), and the Support Vector Machines (SVMs)

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Yang, Yu-Qian, and Cheng-Yi Zhang. "Kernel Based Telegraph-Diffusion Equation for Image Noise Removal." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/283751.

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The second-order partial differential equations have good performances on noise smoothing and edge preservation. However, for low signal-to-noise ratio (SNR) images, the discrimination between edges and noise is a challenging problem. In this paper, the authors propose a kernel based telegraph-diffusion equation (KTDE) for noise removal. In this method, a kernelized gradient operator is introduced in the second-order telegraph-diffusion equation (TDE), which leads to more effective noise removal capability. Experiment results show that this method outperforms several anisotropic diffusion meth

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Cárdenas-Peña, David, Diego Collazos-Huertas, and German Castellanos-Dominguez. "Centered Kernel Alignment Enhancing Neural Network Pretraining for MRI-Based Dementia Diagnosis." Computational and Mathematical Methods in Medicine 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/9523849.

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Dementia is a growing problem that affects elderly people worldwide. More accurate evaluation of dementia diagnosis can help during the medical examination. Several methods for computer-aided dementia diagnosis have been proposed using resonance imaging scans to discriminate between patients with Alzheimer’s disease (AD) or mild cognitive impairment (MCI) and healthy controls (NC). Nonetheless, the computer-aided diagnosis is especially challenging because of the heterogeneous and intermediate nature of MCI. We address the automated dementia diagnosis by introducing a novel supervised pretrain

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Kar, Arindam, Debotosh Bhattacharjee, Dipak Kumar Basu, Mita Nasipuri, and Mahantapas Kundu. "A Gabor-Block-Based Kernel Discriminative Common Vector Approach Using Cosine Kernels for Human Face Recognition." Computational Intelligence and Neuroscience 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/421032.

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In this paper a nonlinear Gabor Wavelet Transform (GWT) discriminant feature extraction approach for enhanced face recognition is proposed. Firstly, the low-energized blocks from Gabor wavelet transformed images are extracted. Secondly, the nonlinear discriminating features are analyzed and extracted from the selected low-energized blocks by the generalized Kernel Discriminative Common Vector (KDCV) method. The KDCV method is extended to include cosine kernel function in the discriminating method. The KDCV with the cosine kernels is then applied on the extracted low-energized discriminating fe

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Ghansah, Benjamin, Ben-Bright Benuwa, and Augustine Monney. "A Discriminative Locality-Sensitive Dictionary Learning With Kernel Weighted KNN Classification for Video Semantic Concepts Analysis." International Journal of Intelligent Information Technologies 17, no. 1 (2021): 68–91. http://dx.doi.org/10.4018/ijiit.2021010105.

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Video semantic concept analysis has received a lot of research attention in the area of human computer interactions in recent times. Reconstruction error classification methods based on sparse coefficients do not consider discrimination, essential for classification performance between video samples. To further improve the accuracy of video semantic classification, a video semantic concept classification approach based on sparse coefficient vector (SCV) and a kernel-based weighted KNN (KWKNN) is proposed in this paper. In the proposed approach, a loss function that integrates reconstruction er

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Feng, Xuping, Yiying Zhao, Chu Zhang, Peng Cheng, and Yong He. "Discrimination of Transgenic Maize Kernel Using NIR Hyperspectral Imaging and Multivariate Data Analysis." Sensors 17, no. 8 (2017): 1894. http://dx.doi.org/10.3390/s17081894.

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Lee, Kun-Chou. "RADAR TARGET RECOGNITION BY FREQUENCY-DIVERSITY RCS TOGETHER WITH KERNEL SCATTER DIFFERENCE DISCRIMINATION." Progress In Electromagnetics Research M 87 (2019): 137–45. http://dx.doi.org/10.2528/pierm19101201.

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REN Xiao-dong, 任晓东, and 雷武虎 LEI Wu-hu. "Kernel Anomaly Detection Method in Hyperspectral Imagery Based on the Spectral Discrimination Method." ACTA PHOTONICA SINICA 45, no. 3 (2016): 330003. http://dx.doi.org/10.3788/gzxb20164503.0330003.

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Ma, Fei, Ju Wang, Changhong Liu, et al. "Discrimination of Kernel Quality Characteristics for Sunflower Seeds Based on Multispectral Imaging Approach." Food Analytical Methods 8, no. 7 (2014): 1629–36. http://dx.doi.org/10.1007/s12161-014-0038-x.

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Baek, Insuck, Dewi Kusumaningrum, Lalit Kandpal, et al. "Rapid Measurement of Soybean Seed Viability Using Kernel-Based Multispectral Image Analysis." Sensors 19, no. 2 (2019): 271. http://dx.doi.org/10.3390/s19020271.

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Viability is an important quality factor influencing seed germination and crop yield. Current seed-viability testing methods rely on conventional manual inspections, which use destructive, labor-intensive and time-consuming measurements. The aim of this study is to distinguish between viable and nonviable soybean seeds, using a near-infrared (NIR) hyperspectral imaging (HSI) technique in a rapid and nondestructive manner. The data extracted from the NIR–HSI of viable and nonviable soybean seeds were analyzed using a partial least-squares discrimination analysis (PLS-DA) technique for classifyi

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Liu, Zhenqiu, Dechang Chen, and Halima Bensmail. "Gene Expression Data Classification With Kernel Principal Component Analysis." Journal of Biomedicine and Biotechnology 2005, no. 2 (2005): 155–59. http://dx.doi.org/10.1155/jbb.2005.155.

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One important feature of the gene expression data is that the number of genesMfar exceeds the number of samplesN. Standard statistical methods do not work well whenN<M. Development of new methodologies or modification of existing methodologies is needed for the analysis of the microarray data. In this paper, we propose a novel analysis procedure for classifying the gene expression data. This procedure involves dimension reduction using kernel principal component analysis (KPCA) and classification with logistic regression (discrimination). KPCA is a generalization and nonlinear version of pr

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Sun, Yubing, Jun Wang, and Shaoming Cheng. "Early Diagnosis of Botrytis Cinerea Infestation of Tomato Plant by Electronic Nose." Applied Engineering in Agriculture 34, no. 4 (2018): 667–74. http://dx.doi.org/10.13031/aea.12748.

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Abstract. Early diagnosis of disease is important for loss control. It is much easier to manage and prevent disease from spreading in this period. This study employed electronic nose (E-nose) for early diagnosis of infestation of tomato plant. Gas Chromatography-Mass Spectrometer (GC-MS) was applied for proving the potential of E-nose detection and taken as the evidence for determining the range of parameters of Kernel Principal Component Analysis (KPCA). Then, the way to seek the best parameter (the type of kernel, kernel parameter, and the number of principal component) of KPCA for the predi

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Kim, Sangkyeum, Kyunghyun Lee, and Kwanho You. "Seismic Discrimination between Earthquakes and Explosions Using Support Vector Machine." Sensors 20, no. 7 (2020): 1879. http://dx.doi.org/10.3390/s20071879.

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The discrimination between earthquakes and explosions is a serious issue in seismic signal analysis. This paper proposes a seismic discrimination method using support vector machine (SVM), wherein the amplitudes of the P-wave and the S-wave of the seismic signals are selected as feature vectors. Furthermore, to improve the seismic discrimination performance using a heterodyne laser interferometer for seismic wave detection, the Hough transform is applied as a compensation method for the periodic nonlinearity error caused by the frequency-mixing in the laser interferometric seismometer. In the

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Goriewa-Duba, Klaudia, Adrian Duba, Urszula Wachowska, and Marian Wiwart. "An Evaluation of the Variation in the Morphometric Parameters of Grain of Six Triticum Species with the Use of Digital Image Analysis." Agronomy 8, no. 12 (2018): 296. http://dx.doi.org/10.3390/agronomy8120296.

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Kernel images of six wheat species were subjected to shape and color analyses to determine variations in the morphometric parameters of grain. The values of kernel shape descriptors (area, perimeter, Feret diameter, minimal Feret diameter, circularity, aspect ratio, roundness, solidity) and color descriptors (H, S, I and L*a*b*) were investigated. The influence of grain colonization by endophytic fungi on the color of the seed coat was also evaluated. Polish wheat grain was characterized by the highest intraspecific variation in shape and color. Bread wheat was most homogeneous in terms of the

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Torres-Valencia, Cristian, Álvaro Orozco, David Cárdenas-Peña, Andrés Álvarez-Meza, and Mauricio Álvarez. "A Discriminative Multi-Output Gaussian Processes Scheme for Brain Electrical Activity Analysis." Applied Sciences 10, no. 19 (2020): 6765. http://dx.doi.org/10.3390/app10196765.

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The study of brain electrical activity (BEA) from different cognitive conditions has attracted a lot of interest in the last decade due to the high number of possible applications that could be generated from it. In this work, a discriminative framework for BEA via electroencephalography (EEG) is proposed based on multi-output Gaussian Processes (MOGPs) with a specialized spectral kernel. First, a signal segmentation stage is executed, and the channels from the EEG are used as the model outputs. Then, a novel covariance function within the MOGP known as the multispectral mixture kernel (MOSM)

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Crnojević, Vladimir, Marko Panić, Branko Brkljač, Dubravko Ćulibrk, Jelena Ačanski, and Ante Vujić. "Image Processing Method for Automatic Discrimination of Hoverfly Species." Mathematical Problems in Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/986271.

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An approach to automatic hoverfly species discrimination based on detection and extraction of vein junctions in wing venation patterns of insects is presented in the paper. The dataset used in our experiments consists of high resolution microscopic wing images of several hoverfly species collected over a relatively long period of time at different geographic locations. Junctions are detected using the combination of the well known HOG (histograms of oriented gradients) and the robust version of recently proposed CLBP (complete local binary pattern). These features are used to train an SVM clas

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Harper, Gavin, John Bradshaw, John C. Gittins, Darren V. S. Green, and Andrew R. Leach. "ChemInform Abstract: Prediction of Biological Activity for High-Throughput Screening Using Binary Kernel Discrimination." ChemInform 32, no. 48 (2010): no. http://dx.doi.org/10.1002/chin.200148231.

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Labbé, Nicole, Seung-Hwan Lee, Hyun-Woo Cho, Myong K. Jeong, and Nicolas André. "Enhanced discrimination and calibration of biomass NIR spectral data using non-linear kernel methods." Bioresource Technology 99, no. 17 (2008): 8445–52. http://dx.doi.org/10.1016/j.biortech.2008.02.052.

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Tang, Yidong, Shucai Huang, and Aijun Xue. "Sparse Representation Based Binary Hypothesis Model for Hyperspectral Image Classification." Mathematical Problems in Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/3460281.

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The sparse representation based classifier (SRC) and its kernel version (KSRC) have been employed for hyperspectral image (HSI) classification. However, the state-of-the-art SRC often aims at extended surface objects with linear mixture in smooth scene and assumes that the number of classes is given. Considering the small target with complex background, a sparse representation based binary hypothesis (SRBBH) model is established in this paper. In this model, a query pixel is represented in two ways, which are, respectively, by background dictionary and by union dictionary. The background dicti

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