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Relevant bibliographies by topics / Kernel discrimination

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Academic literature on the topic 'Kernel discrimination'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 February 2022

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Journal articles on the topic "Kernel discrimination"

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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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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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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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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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Dissertations / Theses on the topic "Kernel discrimination"

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Liang, Zhiyu. "Eigen-analysis of kernel operators for nonlinear dimension reduction and discrimination." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1388676476.

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Shin, Hyejin. "Infinite dimensional discrimination and classification." Texas A&M University, 2003. http://hdl.handle.net/1969.1/5832.

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Modern data collection methods are now frequently returning observations that should be viewed as the result of digitized recording or sampling from stochastic processes rather than vectors of finite length. In spite of great demands, only a few classification methodologies for such data have been suggested and supporting theory is quite limited. The focus of this dissertation is on discrimination and classification in this infinite dimensional setting. The methodology and theory we develop are based on the abstract canonical correlation concept of Eubank and Hsing (2005), and motivated by the

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Harper, Gavin. "The selection of compounds for screening in pharmaceutical research." Thesis, University of Oxford, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326003.

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Lachaud, Antoine. "Discrimination robuste par méthode à noyaux." Thesis, Rouen, INSA, 2015. http://www.theses.fr/2015ISAM0015/document.

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La thèse porte sur l'intégration d éléments explicatifs au sein d'un modèle de classification. Plus précisément la solution proposée se compose de la combinaison entre un algorithme de chemin de régularisation appelé DRSVM et une approche noyau appelée KERNEL BASIS. La première partie de la thèse consiste en l'amélioration d'un algorithme appelé DRSVM à partir d'une reformulation du chemin via la théorie de la sous-différentielle. La seconde partie décrit l'extension de l'algorithme DRSVM au cadre KERNEL BASIS via une approche dictionnaire. Enfin une série d'expérimentation sont réalisées afin

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Suutala, J. (Jaakko). "Learning discriminative models from structured multi-sensor data for human context recognition." Doctoral thesis, Oulun yliopisto, 2012. http://urn.fi/urn:isbn:9789514298493.

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Abstract In this work, statistical machine learning and pattern recognition methods were developed and applied to sensor-based human context recognition. More precisely, we concentrated on an effective discriminative learning framework, where input-output mapping is learned directly from a labeled dataset. Non-parametric discriminative classification and regression models based on kernel methods were applied. They include support vector machines (SVM) and Gaussian processes (GP), which play a central role in modern statistical machine learning. Based on these established models, we propose var

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Grauman, Kristen, and Trevor Darrell. "Pyramid Match Kernels: Discriminative Classification with Sets of Image Features." 2005. http://hdl.handle.net/1721.1/30420.

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Discriminative learning is challenging when examples are setsof local image features, and the sets vary in cardinality and lackany sort of meaningful ordering. Kernel-based classificationmethods can learn complex decision boundaries, but a kernelsimilarity measure for unordered set inputs must somehow solve forcorrespondences -- generally a computationally expensive task thatbecomes impractical for large set sizes. We present a new fastkernel function which maps unordered feature sets tomulti-resolution histograms and computes a weighted histogramintersection in this space. This ``pyramid m

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Grauman, Kristen, and Trevor Darrell. "Pyramid Match Kernels: Discriminative Classification with Sets of Image Features (version 2)." 2006. http://hdl.handle.net/1721.1/31338.

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Discriminative learning is challenging when examples are sets of features, and the sets vary in cardinality and lack any sort of meaningful ordering. Kernel-based classification methods can learn complex decision boundaries, but a kernel over unordered set inputs must somehow solve for correspondences -- generally a computationally expensive task that becomes impractical for largeset sizes. We present a new fast kernel function which maps unordered feature sets to multi-resolution histograms and computes a weighted histogram intersection in this space. This ``pyramid match" computation is l

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Hwang, Sung Ju. "Discriminative object categorization with external semantic knowledge." 2013. http://hdl.handle.net/2152/21320.

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Visual object category recognition is one of the most challenging problems in computer vision. Even assuming that we can obtain a near-perfect instance level representation with the advances in visual input devices and low-level vision techniques, object categorization still remains as a difficult problem because it requires drawing boundaries between instances in a continuous world, where the boundaries are solely defined by human conceptualization. Object categorization is essentially a perceptual process that takes place in a human-defined semantic space. In this semantic space, the catego

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Books on the topic "Kernel discrimination"

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Baillo, Amparo, Antonio Cuevas, and Ricardo Fraiman. Classification methods for functional data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.10.

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This article reviews the literature concerning supervised and unsupervised classification of functional data. It first explains the meaning of unsupervised classification vs. supervised classification before discussing the supervised classification problem in the infinite-dimensional case, showing that its formal statement generally coincides with that of discriminant analysis in the classical multivariate case. It then considers the optimal classifier and plug-in rules, empirical risk and empirical minimization rules, linear discrimination rules, the k nearest neighbor (k-NN) method, and kern

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Book chapters on the topic "Kernel discrimination"

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Gualtierotti, Antonio F. "Reproducing Kernel Hilbert Spaces and Discrimination." In Detection of Random Signals in Dependent Gaussian Noise. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22315-5_5.

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Cárdenas-Peña, D., A. M. Álvarez-Meza, and Germán Castellanos-Domínguez. "Kernel-Based Image Representation for Brain MRI Discrimination." In Advanced Information Systems Engineering. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-319-12568-8_42.

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Chen, Jiada, Jianhuang Lai, and Guocan Feng. "Gabor-Based Kernel Fisher Discriminant Analysis for Pose Discrimination." In Advances in Biometric Person Authentication. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30548-4_18.

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Liu, Xiuwen, and Washington Mio. "Kernel Methods for Nonlinear Discriminative Data Analysis." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11585978_38.

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Cheng, Keyang, Qirong Mao, and Yongzhao Zhan. "Pedestrian Detection Based on Kernel Discriminative Sparse Representation." In Transactions on Edutainment IX. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37042-7_12.

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Tommasi, Tatiana, Elisabetta La Torre, and Barbara Caputo. "Melanoma Recognition Using Representative and Discriminative Kernel Classifiers." In Computer Vision Approaches to Medical Image Analysis. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11889762_1.

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Roth, Volker. "Probabilistic Discriminative Kernel Classifiers for Multi-Class Problems." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45404-7_33.

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Lei, Zhenchun, Yingchun Yang, and Zhaohui Wu. "Speaker Identification Using the VQ-Based Discriminative Kernels." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11527923_83.

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Wang, Xiaoying, Le Liu, and Haifeng Hu. "Coupled Kernel Fisher Discriminative Analysis for Low-Resolution Face Recognition." In Biometric Recognition. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02961-0_10.

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Young, Jonathan, Du Lei, and Andrea Mechelli. "Discriminative Log-Euclidean Kernels for Learning on Brain Networks." In Connectomics in NeuroImaging. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67159-8_4.

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Conference papers on the topic "Kernel discrimination"

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Liu, Benyong. "Kernel discrimination via oblique projection." In 2011 International Conference on Image Analysis and Signal Processing (IASP). IEEE, 2011. http://dx.doi.org/10.1109/iasp.2011.6109140.

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Iosifidis, Alexandros, Anastasios Tefas, and Ioannis Pitas. "Enhancing class discrimination in Kernel Discriminant Analysis." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178306.

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Oikonomou, Vangelis P., Spiros Nikolopoulos, and Ioannis Kompatsiaris. "Discrimination of SSVEP responses using a kernel based approach." In 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2019. http://dx.doi.org/10.1109/embc.2019.8857685.

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Wang, Guangbin, and Liangpei Huang. "Kernel orthogonal local fisher discrimination for rotor fault diagnosis." In International Conference on Image Processing and Pattern Recognition in Industrial Engineering, edited by Zhengyu Du and Bin Liu. SPIE, 2010. http://dx.doi.org/10.1117/12.867052.

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Kumar, Ritwik, Ting Chen, Moritz Hardt, David Beymer, Karen Brannon, and Tanveer Syeda-Mahmood. "Multiple Kernel Completion and its application to cardiac disease discrimination." In 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013). IEEE, 2013. http://dx.doi.org/10.1109/isbi.2013.6556587.

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Alvarez, Marco A., and Changhui Yan. "Exploring structural modeling of proteins for kernel-based enzyme discrimination." In 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2010. http://dx.doi.org/10.1109/cibcb.2010.5510588.

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Tbarki, Khaoula, Salma Ben Said, Riadh Ksantini, and Zied Lachiri. "RBF kernel based SVM classification for landmine detection and discrimination." In 2016 International Image Processing, Applications and Systems (IPAS). IEEE, 2016. http://dx.doi.org/10.1109/ipas.2016.7880146.

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Alvarez-Meza, A. M., D. Cardenas-Pena, and G. Castellanos-Dominguez. "MRI discrimination by inter-slice similarities and kernel-based centered alignment." In 2014 International Work Conference on Bio-inspired Intelligence (IWOBI). IEEE, 2014. http://dx.doi.org/10.1109/iwobi.2014.6913955.

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Xu, Zheng. "Financial Early-Warning Model Based on Q-Gaussian Kernel Fisher Discrimination." In 2014 6th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). IEEE, 2014. http://dx.doi.org/10.1109/ihmsc.2014.147.

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Cheng, J., X. Chen, X. Liu, S. Chen, and C. Li. "Lithofacies Discrimination Based On Adaptive Kernel Function of Support Vector Machines." In 80th EAGE Conference and Exhibition 2018. EAGE Publications BV, 2018. http://dx.doi.org/10.3997/2214-4609.201800921.

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