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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 kernel Δ (P≤0.001), even in the driest environment, which gave a mean yield of 1.5 t ha-1. For the four parameters, correlations with yield remained significant (P≤0.001) after correcting for days to heading. All the parameters showed a higher broad-sense heritability than yield. The parameter that showed the best genetic correlation with grain yield was kernel ash (r2= 0.88), followed by kernel Δ (r2 = 0.69) and leaf ash (r2 = 0.64), whereas leaf Δ (r2 = 0.26) was the least correlated parameter. Except for kernel ash, these parameters always correlated positively with grain yield. The negative relationships of kernel ash (on dry mass basis) with yield and all the other parameters may be attributable to the finding that kernel ash was higher in those genotypes more affected by drought during grain filling. Thus, kernel ash was negatively related (P≤0.001) with total kernel mass per spike. Prediction of grain yield through multiple linear regression suggests that kernel ash can be used as complementary criterion to either kernel Δ or leaf ash.

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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 calculates a small kernel matrix by constructing a few mean samples in input space, then expresses the kernel discriminant vectors using MMS. The proposed kernel approach is tested on the public AR and FERET face databases. Experimental results show that this approach is effective in both saving computing time and acquiring favorable recognition results.

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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 (SVMs) is proposed. The stem kernel is a natural extension of the string kernel, specifically the all-subsequences kernel, and is tailored to measure the similarity of two RNA sequences from the viewpoint of secondary structures. The stem kernel examines all possible common base pairs and stem structures of arbitrary lengths, including pseudoknots between two RNA sequences, and calculates the inner product of common stem structure counts. An efficient algorithm is developed to calculate the stem kernels based on dynamic programming. The stem kernels are then applied to discriminate members of an RNA family from nonmembers using SVMs. The study indicates that the discrimination ability of the stem kernel is strong compared with conventional methods. Furthermore, the potential application of the stem kernel is demonstrated by the detection of remotely homologous RNA families in terms of secondary structures. This is because the string kernel is proven to work for the remote homology detection of protein sequences. These experimental results have convinced us to apply the stem kernel in order to find novel RNA families from genome sequences.

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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 (DON) content was determined in individual kernels using an ELISA method. Images of individual kernels were produced using a digital camera on dark background. Colour and shape descriptors were obtained by image analysis from the area representing the kernel. Healthy and damaged kernels differed significantly in DON content and kernel weight. Various combinations of individual shape and colour descriptors were examined during the development of the model using linear discriminant analysis. In addition to basic descriptors of the RGB colour model (red, green, blue), very good classification was also obtained using hue from the HSL colour model (hue, saturation, luminance). The accuracy of classification using the developed discrimination model based on RGBH descriptors was 85 %. The shape descriptors themselves were not specific enough to distinguish individual kernels.

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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 and oil. By contrast, spectral features of proteins, polysaccharides, and tannins predominated in the FT-NIR spectra of flaxseed hulls and defatted flours.

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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 spectrally-derived moisture variable (Log[1/R972nm]-Log[1/R1032nm]) and starch variable (Log[1/R982nm]-Log[1/R1014nm]), it was possible to discriminate between infested and uninfested wheat kernels with 100% classification accuracy based on 90% confidence intervals. Significant differences in the spectral reflectance between the infested and uninfested kernels were due to changes in moisture and starch content in wheat kernels. Three of the four chitin variables showed slight discrimination between the infested and uninfested wheat kernels based on 90% confidence intervals with 63.9%, 68.8%, 66.7%, and 41.6% classification accuracy of the three variables (Log[1/R1130nm]-Log[1/R1670nm]), (Log[1/R1139nm ]-Log[1/R1320nm]), (Log[1/R1202nm]-Log[1/R1300nm]), and (Log[1/R2046nm]-Log[1/R2302nm]), respectively. Spectral reflectance changes as a function of wheat kernel position relative to the spectroradiometer sensor did not differ significantly (P &gt; 0.10).

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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 input to PLS-DA and SVM algorithms to construct discriminative models for aflatoxin contamination in peanut oil. SVM penalty and kernel function parameters were optimized using grid search, a genetic algorithm, and particle swarm optimization. The PLS-DA model established using spectral data had an accuracy of 94.64% and better discrimination than did models established based on preprocessed data. The SVM model established after data normalization and grid search optimization with a penalty parameter of 16 and a kernel function parameter of 0.0359 had the best discrimination, with 98.2143% accuracy. The discriminative models for aflatoxin contamination in peanut oil established by combining ATR-FTIR spectral data and nonlinear SVM algorithm were superior to the linear PLS-DA models. HIGHLIGHTS

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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 discriminant functions for diagnostics are presented. Cross-validation and leave-one-out errors were used as measures of classifier performance to support diagnosis coming from this genomic data set. A small number of best discriminating genes, from two to ten, was sufficient to build discriminant methods of good performance. Especially useful were nearest neighbor methods. The results presented herein were comparable with outcomes obtained by other authors for larger numbers of applied genes. The linear, quadratic, uncorrelated Bayesian and regularized discrimination methods were subjected to bagging or boosting in order to assess the accuracy of the fusion. A conclusion drawn from the analysis was that resampling ensembles were not beneficial for two-dimensional discrimination.

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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 fact that Fisher's discriminant analysis method is intimately tied to canonical correlation analysis. Specifically, we have developed a theoretical framework for discrimination and classification of sample paths from stochastic processes through use of the Loeve-Parzen isomorphism that connects a second order process to the reproducing kernel Hilbert space generated by its covariance kernel. This approach provides a seamless transition between the finite and infinite dimensional settings and lends itself well to computation via smoothing and regularization. In addition, we have developed a new computational procedure and illustrated it with simulated data and Canadian weather data.

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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 de valider l'aspect interprétable du modèle<br>This thesis aims at finding classification rnodeIs which include explanatory elements. More specifically the proposed solution consists in merging a regularization path algorithm called DRSVM with a kernel approach called KERNEL BASIS. The first part of the thesis focuses on improving an algorithm called DRSVM from a reformulation of the thanks to the suh-differential theory. The second part of the thesis describes the extension of DRSVM afgorithm under a KERNEL BASIS framework via a dictionary approach. Finally, a series of experiments are conducted in order to validate the interpretable aspect of the rnodel

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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 various extensions for handling structured data that usually arise from real-life applications, for example, in a field of context-aware computing. We applied both SVM and GP techniques to handle data with multiple classes in a structured multi-sensor domain. Moreover, a framework for combining data from several sources in this setting was developed using multiple classifiers and fusion rules, where kernel methods are used as base classifiers. We developed two novel methods for handling sequential input and output data. For sequential time-series data, a novel kernel based on graphical presentation, called a weighted walk-based graph kernel (WWGK), is introduced. For sequential output labels, discriminative temporal smoothing (DTS) is proposed. Again, the proposed algorithms are modular, so different kernel classifiers can be used as base models. Finally, we propose a group of techniques based on Gaussian process regression (GPR) and particle filtering (PF) to learn to track multiple targets. We applied the proposed methodology to three different human-motion-based context recognition applications: person identification, person tracking, and activity recognition, where floor (pressure-sensitive and binary switch) and wearable acceleration sensors are used to measure human motion and gait during walking and other activities. Furthermore, we extracted a useful set of specific high-level features from raw sensor measurements based on time, frequency, and spatial domains for each application. As a result, we developed practical extensions to kernel-based discriminative learning to handle many kinds of structured data applied to human context recognition<br>Tiivistelmä Tässä työssä kehitettiin ja sovellettiin tilastollisen koneoppimisen ja hahmontunnistuksen menetelmiä anturipohjaiseen ihmiseen liittyvän tilannetiedon tunnistamiseen. Esitetyt menetelmät kuuluvat erottelevan oppimisen viitekehykseen, jossa ennustemalli sisääntulomuuttujien ja vastemuuttujan välille voidaan oppia suoraan tunnetuilla vastemuuttujilla nimetystä aineistosta. Parametrittomien erottelevien mallien oppimiseen käytettiin ydinmenetelmiä kuten tukivektorikoneita (SVM) ja Gaussin prosesseja (GP), joita voidaan pitää yhtenä modernin tilastollisen koneoppimisen tärkeimmistä menetelmistä. Työssä kehitettiin näihin menetelmiin liittyviä laajennuksia, joiden avulla rakenteellista aineistoa voidaan mallittaa paremmin reaalimaailman sovelluksissa, esimerkiksi tilannetietoisen laskennan sovellusalueella. Tutkimuksessa sovellettiin SVM- ja GP-menetelmiä moniluokkaisiin luokitteluongelmiin rakenteellisen monianturitiedon mallituksessa. Useiden tietolähteiden käsittelyyn esitetään menettely, joka yhdistää useat opetetut luokittelijat päätöstason säännöillä lopulliseksi malliksi. Tämän lisäksi aikasarjatiedon käsittelyyn kehitettiin uusi graafiesitykseen perustuva ydinfunktio sekä menettely sekventiaalisten luokkavastemuuttujien käsittelyyn. Nämä voidaan liittää modulaarisesti ydinmenetelmiin perustuviin erotteleviin luokittelijoihin. Lopuksi esitetään tekniikoita usean liikkuvan kohteen seuraamiseen. Menetelmät perustuvat anturitiedosta oppivaan GP-regressiomalliin ja partikkelisuodattimeen. Työssä esitettyjä menetelmiä sovellettiin kolmessa ihmisen liikkeisiin liittyvässä tilannetiedon tunnistussovelluksessa: henkilön biometrinen tunnistaminen, henkilöiden seuraaminen sekä aktiviteettien tunnistaminen. Näissä sovelluksissa henkilön asentoa, liikkeitä ja astuntaa kävelyn ja muiden aktiviteettien aikana mitattiin kahdella erilaisella paineherkällä lattia-anturilla sekä puettavilla kiihtyvyysantureilla. Tunnistusmenetelmien laajennuksien lisäksi jokaisessa sovelluksessa kehitettiin menetelmiä signaalin segmentointiin ja kuvaavien piirteiden irroittamiseen matalantason anturitiedosta. Tutkimuksen tuloksena saatiin parannuksia erottelevien mallien oppimiseen rakenteellisesta anturitiedosta sekä erityisesti uusia menettelyjä tilannetiedon tunnistamiseen

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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 match" computation islinear in the number of features, and it implicitly findscorrespondences based on the finest resolution histogram cell wherea matched pair first appears. Since the kernel does not penalize thepresence of extra features, it is robust to clutter. We show thekernel function is positive-definite, making it valid for use inlearning algorithms whose optimal solutions are guaranteed only forMercer kernels. We demonstrate our algorithm on object recognitiontasks and show it to be dramatically faster than currentapproaches.

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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 linear in the number of features, and it implicitly finds correspondences based on the finest resolution histogram cell where a matched pair first appears. Since the kerneldoes not penalize the presence of extra features, it is robust to clutter. We show the kernel function is positive-definite, making it valid for use in learning algorithms whose optimal solutions are guaranteed only for Mercer kernels. We demonstrate our algorithm on object recognition tasks and show it to be accurate and dramatically faster than current approaches. (This tech report updates MIT-CSAIL-TR-2005-017 and the paper "The Pyramid Match Kernel: Discriminative Classification with Sets of Images Features" which appeared in the proceedings of ICCV 2005.)

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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 categories reside not in isolation, but in relation to others. Some categories are similar, grouped, or co-occur, and some are not. However, despite this semantic nature of object categorization, most of the today's automatic visual category recognition systems rely only on the category labels for training discriminative recognition with statistical machine learning techniques. In many cases, this could result in the recognition model being misled into learning incorrect associations between visual features and the semantic labels, from essentially overfitting to training set biases. This limits the model's prediction power when new test instances are given. Using semantic knowledge has great potential to benefit object category recognition. First, semantic knowledge could guide the training model to learn a correct association between visual features and the categories. Second, semantics provide much richer information beyond the membership information given by the labels, in the form of inter-category and category-attribute distances, relations, and structures. Finally, the semantic knowledge scales well as the relations between categories become larger with an increasing number of categories. My goal in this thesis is to learn discriminative models for categorization that leverage semantic knowledge for object recognition, with a special focus on the semantic relationships among different categories and concepts. To this end, I explore three semantic sources, namely attributes, taxonomies, and analogies, and I show how to incorporate them into the original discriminative model as a form of structural regularization. In particular, for each form of semantic knowledge I present a feature learning approach that defines a semantic embedding to support the object categorization task. The regularization penalizes the models that deviate from the known structures according to the semantic knowledge provided. The first semantic source I explore is attributes, which are human-describable semantic characteristics of an instance. While the existing work treated them as mid-level features which did not introduce new information, I focus on their potential as a means to better guide the learning of object categories, by enforcing the object category classifiers to share features with attribute classifiers, in a multitask feature learning framework. This approach essentially discovers the common low-dimensional features that support predictions in both semantic spaces. Then, I move on to the semantic taxonomy, which is another valuable source of semantic knowledge. The merging and splitting criteria for the categories on a taxonomy are human-defined, and I aim to exploit this implicit semantic knowledge. Specifically, I propose a tree of metrics (ToM) that learns metrics that capture granularity-specific similarities at different nodes of a given semantic taxonomy, and uses a regularizer to isolate granularity-specific disjoint features. This approach captures the intuition that the features used for the discrimination of the parent class should be different from the features used for the children classes. Such learned metrics can be used for hierarchical classification. The use of a single taxonomy can be limited in that its structure is not optimal for hierarchical classification, and there may exist no single optimal semantic taxonomy that perfectly aligns with visual distributions. Thus, I next propose a way to overcome this limitation by leveraging multiple taxonomies as semantic sources to exploit, and combine the acquired complementary information across multiple semantic views and granularities. This allows us, for example, to synthesize semantics from both 'Biological', and 'Appearance'-based taxonomies when learning the visual features. Finally, as a further exploration of more complex semantic relations different from the previous two pairwise similarity-based models, I exploit analogies, which encode the relational similarities between two related pairs of categories. Specifically, I use analogies to regularize a discriminatively learned semantic embedding space for categorization, such that the displacements between the two category embeddings in both category pairs of the analogy are enforced to be the same. Such a constraint allows for a more confusing pair of categories to benefit from a clear separation in the matched pair of categories that share the same relation. All of these methods are evaluated on challenging public datasets, and are shown to effectively improve the recognition accuracy over purely discriminative models, while also guiding the recognition to be more semantic to human perception. Further, the applications of the proposed methods are not limited to visual object categorization in computer vision, but they can be applied to any classification problems where there exists some domain knowledge about the relationships or structures between the classes. Possible applications of my methods outside the visual recognition domain include document classification in natural language processing, and gene-based animal or protein classification in computational biology.<br>text

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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 kernel rules. It also describes classification based on partial least squares, classification based on reproducing kernels, and depth-based classification. Finally, it examines unsupervised classification methods, focusing on K-means for functional data, K-means for data in a Hilbert space, and impartial trimmed K-means for functional data. Some practical issues, in particular real-data examples and simulations, are reviewed and some selected proofs are given.

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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"

1

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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