Relevant bibliographies by topics / Robust Classification

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Robust Classification'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 June 2025

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Journal articles on the topic "Robust Classification"

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Bertsimas, Dimitris, Jack Dunn, Colin Pawlowski, and Ying Daisy Zhuo. "Robust Classification." INFORMS Journal on Optimization 1, no. 1 (2019): 2–34. http://dx.doi.org/10.1287/ijoo.2018.0001.

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Chen, Li, Cencheng Shen, Joshua T. Vogelstein, and Carey E. Priebe. "Robust Vertex Classification." IEEE Transactions on Pattern Analysis and Machine Intelligence 38, no. 3 (2016): 578–90. http://dx.doi.org/10.1109/tpami.2015.2456913.

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Glendinning, R. H. "Robust shape classification." Signal Processing 77, no. 2 (1999): 121–38. http://dx.doi.org/10.1016/s0165-1684(99)00028-6.

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Addison, W. D., and R. H. Glendinning. "Robust image classification." Signal Processing 86, no. 7 (2006): 1488–501. http://dx.doi.org/10.1016/j.sigpro.2005.08.010.

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Zhang, Jun, Xiao Chen, Yang Xiang, Wanlei Zhou, and Jie Wu. "Robust Network Traffic Classification." IEEE/ACM Transactions on Networking 23, no. 4 (2015): 1257–70. http://dx.doi.org/10.1109/tnet.2014.2320577.

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Katz, Alan J., Michael T. Gately, and Dean R. Collins. "Robust Classifiers without Robust Features." Neural Computation 2, no. 4 (1990): 472–79. http://dx.doi.org/10.1162/neco.1990.2.4.472.

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We develop a two-stage, modular neural network classifier and apply it to an automatic target recognition problem. The data are features extracted from infrared and TV images. We discuss the problem of robust classification in terms of a family of decision surfaces, the members of which are functions of a set of global variables. The global variables characterize how the feature space changes from one image to the next. We obtain rapid training times and robust classification with this modular neural network approach.
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Lai, Yu-kun, Qian-yi Zhou, Shi-min Hu, Johannes Wallner, and Helmut Pottmann. "Robust Feature Classification and Editing." IEEE Transactions on Visualization and Computer Graphics 13, no. 1 (2007): 34–45. http://dx.doi.org/10.1109/tvcg.2007.19.

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Hubert, Mia, and Stephan Van der Veeken. "Robust classification for skewed data." Advances in Data Analysis and Classification 4, no. 4 (2010): 239–54. http://dx.doi.org/10.1007/s11634-010-0066-3.

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Kundur, D., D. Hatzinakos, and H. Leung. "Robust classification of blurred imagery." IEEE Transactions on Image Processing 9, no. 2 (2000): 243–55. http://dx.doi.org/10.1109/83.821737.

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Zhang, Lingling, Minnan Luo, Zhihui Li, et al. "Large-Scale Robust Semisupervised Classification." IEEE Transactions on Cybernetics 49, no. 3 (2019): 907–17. http://dx.doi.org/10.1109/tcyb.2018.2789420.

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Dissertations / Theses on the topic "Robust Classification"

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Ge, Zongyuan. "Robust fine-grained image classification." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/107700/1/Zongyuan_Ge_Thesis.pdf.

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This thesis tackles fine-grained image recognition, the task of sub-category or species classification. It explores general methods to improve fine-grained image classification including the use of generative models and deep convolutional neural networks leading to novel models such as a Mixture of deep convolution neural networks. This work led to 9 peer reviewed publications and a Best Paper Award.
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Podder, Mohua. "Robust genotype classification using dynamic variable selection." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/1602.

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Single nucleotide polymorphisms (SNPs) are DNA sequence variations, occurring when a single nucleotide –A, T, C or G – is altered. Arguably, SNPs account for more than 90% of human genetic variation. Dr. Tebbutt's laboratory has developed a highly redundant SNP genotyping assay consisting of multiple probes with signals from multiple channels for a single SNP, based on arrayed primer extension (APEX). The strength of this platform is its unique redundancy having multiple probes for a single SNP. Using this microarray platform, we have developed fully-automated genotype calling algorithms based on linear models for individual probe signals and using dynamic variable selection at the prediction level. The algorithms combine separate analyses based on the multiple probe sets to give a final confidence score for each candidate genotypes. Our proposed classification model achieved an accuracy level of >99.4% with 100% call rate for the SNP genotype data which is comparable with existing genotyping technologies. We discussed the appropriateness of the proposed model related to other existing high-throughput genotype calling algorithms. In this thesis we have explored three new ideas for classification with high dimensional data: (1) ensembles of various sets of predictors with built-in dynamic property; (2) robust classification at the prediction level; and (3) a proper confidence measure for dealing with failed predictor(s). We found that a mixture model for classification provides robustness against outlying values of the explanatory variables. Furthermore, the algorithm chooses among different sets of explanatory variables in a dynamic way, prediction by prediction. We analyzed several data sets, including real and simulated samples to illustrate these features. Our model-based genotype calling algorithm captures the redundancy in the system considering all the underlying probe features of a particular SNP, automatically down-weighting any ‘bad data’ corresponding to image artifacts on the microarray slide or failure of a specific chemistry. Though motivated by this genotyping application, the proposed methodology would apply to other classification problems where the explanatory variables fall naturally into groups or outliers in the explanatory variables require variable selection at the prediction stage for robustness.
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Chu, Wei 1966. "Auditory-based noise-robust audio classification algorithms." Thesis, McGill University, 2008. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=115863.

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The past decade has seen extensive research on audio classification algorithms which playa key role in multimedia applications, such as the retrieval of audio information from an audio or audiovisual database. However, the effect of background noise on the performance of classification has not been widely investigated. Motivated by the noise-suppression property of the early auditory (EA) model presented by Wang and Shamma, we seek in this thesis to further investigate this property and to develop improved algorithms for audio classification in the presence of background noise.<br>With respect to the limitation of the original analysis, a better yet mathematically tractable approximation approach is first proposed wherein the Gaussian cumulative distribution function is used to derive a new closed-form expression of the auditory spectrum at the output of the EA model, and to conduct relevant analysis. Considering the computational complexity of the original EA model, a simplified auditory spectrum is proposed, wherein the underlying analysis naturally leads to frequency-domain approximation for further reduction in the computational complexity. Based on this time-domain approximation, a simplified FFT-based spectrum is proposed wherein a local spectral self-normalization is implemented. An improved implementation of this spectrum is further proposed to calculate a so-called FFT-based auditory spectrum, which allows more flexibility in the extraction of noise-robust audio features.<br>To evaluate the performance of the above FFT-based spectra, speech/music/noise and noise/non-noise classification experiments are conducted wherein a support vector machine algorithm (SVMstruct) and a decision tree learning algorithm (C4.5) are used as the classifiers. Several features are used for the classification, including the conventional mel-frequency cepstral coefficient (MFCC) features as well as DCT-based and spectral features derived from the proposed FFT-based spectra. Compared to the conventional features, the auditory-related features show more robust performance in mismatched test cases. Test results also indicate that the performance of the proposed FFT-based auditory spectrum is slightly better than that of the original auditory spectrum, while its computational complexity is reduced by an order of magnitude.<br>Finally, to further explore the proposed FFT-based auditory spectrum from a practical audio classification perspective, a floating-point DSP implementation is developed and optimized on the TMS320C6713 DSP Starter Kit (DSK) from Texas Instruments.
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Hu, Hong. "Accurate and robust algorithms for microarray data classification." University of Southern Queensland, Faculty of Sciences, 2008. http://eprints.usq.edu.au/archive/00006221/.

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[Abstract]Microarray data classification is used primarily to predict unseen data using a model built on categorized existing Microarray data. One of the major challenges is that Microarray data contains a large number of genes with a small number of samples. This high dimensionality problem has prevented many existing classification methods from directly dealing with this type of data. Moreover, the small number of samples increases the overfitting problem of Classification, as a result leading to lower accuracy classification performance. Another major challenge is that of the uncertainty of Microarraydata quality. Microarray data contains various levels of noise and quite often high levels of noise, and these data lead to unreliable and low accuracy analysis as well as the high dimensionality problem. Most current classification methods are not robust enough to handle these type of data properly.In our research, accuracy and noise resistance or robustness issues are focused on. Our approach is to design a robust classification method for Microarray data classification.An algorithm, called diversified multiple decision trees (DMDT) is proposed, which makes use of a set of unique trees in the decision committee. The DMDT method has increased the diversity of ensemble committees andtherefore the accuracy performance has been enhanced by avoiding overlapping genes among alternative trees.Some strategies to eliminate noisy data have been looked at. Our method ensures no overlapping genes among alternative trees in an ensemble committee, so a noise gene included in the ensemble committee can affect onetree only; other trees in the committee are not affected at all. This design increases the robustness of Microarray classification in terms of resistance to noise data, and therefore reduces the instability caused by overlapping genes in current ensemble methods.The effectiveness of gene selection methods for improving the performance of Microarray classification methods are also discussed.We conclude that the proposed method DMDT substantially outperforms the other well-known ensemble methods, such as Bagging, Boosting and Random Forests, in terms of accuracy and robustness performance. DMDT is more tolerant to noise than Cascading-and-Sharing trees (CS4), particularywith increasing levels of noise in the data. The results also indicate that some classification methods are insensitive to gene selection while some methodsdepend on particular gene selection methods to improve their performance of classification.
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Tran, Brandon Vanhuy. "Building and using robust representations in image classification." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/127912.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, May, 2020<br>Cataloged from the official PDF of thesis.<br>Includes bibliographical references (pages 115-131).<br>One of the major appeals of the deep learning paradigm is the ability to learn high-level feature representations of complex data. These learned representations obviate manual data pre-processing, and are versatile enough to generalize across tasks. However, they are not yet capable of fully capturing abstract, meaningful features of the data. For instance, the pervasiveness of adversarial examples--small perturbations of correctly classified inputs causing model misclassification--is a prominent indication of such shortcomings. The goal of this thesis is to work towards building learned representations that are more robust and human-aligned. To achieve this, we turn to adversarial (or robust) training, an optimization technique for training networks less prone to adversarial inputs. Typically, robust training is studied purely in the context of machine learning security (as a safeguard against adversarial examples)--in contrast, we will cast it as a means of enforcing an additional prior onto the model. Specifically, it has been noticed that, in a similar manner to the well-known convolutional or recurrent priors, the robust prior serves as a "bias" that restricts the features models can use in classification--it does not allow for any features that change upon small perturbations. We find that the addition of this simple prior enables a number of downstream applications, from feature visualization and manipulation to input interpolation and image synthesis. Most importantly, robust training provides a simple way of interpreting and understanding model decisions. Besides diagnosing incorrect classification, this also has consequences in the so-called "data poisoning" setting, where an adversary corrupts training samples with the hope of causing misbehaviour in the resulting model. We find that in many cases, the prior arising from robust training significantly helps in detecting data poisoning.<br>by Brandon Vanhuy Tran.<br>Ph. D.<br>Ph.D. Massachusetts Institute of Technology, Department of Mathematics
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Siméoni, Oriane. "Robust image representation for classification, retrieval and object discovery." Thesis, Rennes 1, 2020. https://ged.univ-rennes1.fr/nuxeo/site/esupversions/415eb65b-d5f7-4be7-85e6-c2ecb2aba4dc.

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Les réseaux de neurones convolutifs (CNNs) ont été exploités avec succès pour la résolution de tâches dans le domaine de la vision par ordinateur tels que la classification, la segmentation d'image, la détection d'objets dans une image ou la recherche d'images dans une base de données. Typiquement, un réseau est entraîné spécifiquement pour une tâche et l'entraînement nécessite une très grande quantité d'images annotées. Dans cette thèse, nous proposons des solutions pour extraire le maximum d'information avec un minimum de supervision. D'abord, nous nous concentrons sur la tâche de classification en examinant le processus d'apprentissage actif dans le contexte de l'apprentissage profond. Nous montrons qu'en combinant l'apprentissage actif aux techniques d'apprentissage semi-supervisé et non supervisé, il est possible d'améliorer significativement les résultats. Ensuite, nous étudions la tâche de recherche d'images dans une base de données et nous exploitons les informations de localisation spatiale disponible directement dans les cartes d'activation produites par les CNNs. En première approche, nous proposons de représenter une image par une collection de caractéristiques locales, détectées dans les cartes, qui sont peu coûteuses en terme de mémoire et assez robustes pour effectuer une mise en correspondance spatiale. Alternativement, nous découvrons dans les cartes d'activation les objets d'intérêts des images d'une base de données et nous structurons leurs représentations dans un graphe de plus proches voisins. En utilisant la mesure de centralité du graphe, nous sommes capable de construire une carte de saillance, par image, qui met en lumière les objets qui se répètent et nous permet de construire une représentation globale qui exclue les objets non pertinents et d'arrière-plan<br>Neural network representations proved to be relevant for many computer vision tasks such as image classification, object detection, segmentation or instance-level image retrieval. A network is trained for one particular task and requires a large number of labeled data. We propose in this thesis solutions to extract the most information with the least supervision. First focusing on the classification task, we examine the active learning process in the context of deep learning and show that combining it to semi-supervised and unsupervised techniques boost greatly results. We then investigate the image retrieval task, and in particular we exploit the spatial localization information available ``for free'' in CNN feature maps. We first propose to represent an image by a collection of affine local features detected within activation maps, which are memory-efficient and robust enough to perform spatial matching. Then again extracting information from feature maps, we discover objects of interest in images of a dataset and gather their representations in a nearest neighbor graph. Using the centrality measure on the graph, we are able to construct a saliency map per image which focuses on the repeating objects and allows us to compute a global representation excluding clutter and background
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ALI, ARSLAN. "Deep learning techniques for biometric authentication and robust classification." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2910084.

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He, Jin. "Robust Mote-Scale Classification of Noisy Data via Machine Learning." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1440413201.

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Carranza, Alarcón Yonatan Carlos. "Distributionally robust, skeptical inferences in supervised classification using imprecise probabilities." Thesis, Compiègne, 2020. http://www.theses.fr/2020COMP2567.

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Les décideurs sont souvent confrontés au défi de prendre des décisions précises, sans avoir aucune connaissance de la quantité d’incertitudes que celles-ci peuvent contenir, et en prenant le risque de commettre des erreurs dommageables, voire dramatiques. Dans de telles situations, où l’incertitude est plus élevée due à des informations imparfaites, il peut être plutôt utile de fournir des décisions prudentes, sous la forme d’un ensemble de solutions possibles, plus fiables. Ce travail se concentre donc sur la prise de décisions (ou inférences) sceptiques (ou prudentes) et robustes dans des problèmes de classification supervisée en utilisant des probabilités imprécises. Par robuste, nous voulons dire que nous considérons un ensemble des distributions de probabilités possibles, c'est-à-dire des probabilités imprécises, et par sceptique, nous voulons dire que nous ne considérons comme valides que les décisions étant vraies pour chaque distribution dans cet ensemble. Plus précisément, nous nous concentrons sur l'extension d’approches basée sur l'analyse discriminante gaussienne et la classification multi-étiquettes au cadre probabiliste imprécis. Concernant l'analyse discriminante gaussienne, nous proposons un nouveau classifieur imprécis qui généralise celui-ci et qui est basé sur l’inférence bayésienne robuste et un ensemble des lois de probabilités a priori. L’inclusion d’un composant imprécis dans notre approche met en évidence les décisions difficiles à prendre (c.-à-d. les observations difficiles à classifier), sur lesquelles les modèles précis font des erreurs, et permet de fournir à la place des décisions prudentes. Concernant la classification multi-étiquettes, nous nous concentrons d’abord sur la réduction de la complexité calculatoire de prendre une décision prudente sur son espace de sortie combinatoire. Pour cela, nous fournissons des justifications théoriques et des algorithmes efficaces appliqués à la fonction de coût Hamming. En outre, en relâchant l’hypothèse d’indépendance sur les étiquettes, on obtient de décisions partielles (c.-à-d. ne pas décider sur certaines étiquettes), qui généralisent l’approche classique précise (nommé « binary relevance ») en utilisant des distributions marginales imprécises. D’autre part, nous proposons aussi d’étendre le chaînage multi-étiquette classique au cadre probabiliste imprécis en fournissant deux stratégies différentes pour gérer les estimations imprécises sous la forme d’intervalles, et une nouvelle procédure d’ordre des étiquettes qui dépend des incertitudes associées aux étiquettes sélectionnées au fur et à mesure que la chaîne avance<br>Decision makers are often faced with making single hard decisions, without having any knowledge of the amount of uncertainties contained in them, and taking the risk of making damaging, if not dramatic, mistakes. In such situations, where the uncertainty is higher due to imperfect information, it may be useful to provide set-valued but more reliable decisions. This works thus focuses on making distributionally robust, skeptical inferences (or decisions) in supervised classification problems using imprecise probabilities. By distributionally robust, we mean that we consider a set of possible probability distributions, i.e. imprecise probabilities, and by skeptical we understand that we consider as valid only those inferences that are true for every distribution within this set. Specifically, we focus on extending the Gaussian discriminant analysis and multilabel classification approaches to the imprecise probabilistic setting. Regarding to Gaussian discriminant analysis, we extend it by proposing a new imprecise classifier, considering the imprecision as part of its basic axioms, based on robust Bayesian analysis and near-ignorance priors. By including an imprecise component in the model, our proposal highlights those hard instances on which the precise model makes mistakes in order to provide cautious decisions in the form of set-valued class, instead. Regarding to multi-label classification, we first focus on reducing the time complexity of making a cautious decision over its output space of exponential size by providing theoretical justifications and efficient algorithms applied to the Hamming loss. Relaxing the assumption of independence on labels, we obtain partial decisions, i.e. not classifying at all over some labels, which generalize the binary relevance approach by using imprecise marginal distributions. Secondly, we extend the classifierchains approach by proposing two different strategies to handle imprecise probabilityestimates, and a new dynamic, context-dependent label ordering which dynamically selects the labels with low uncertainty as the chain moves forwards
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Szekely, Robin [Verfasser]. "Robust and nonparametric classification of gene expression data / Robin Szekely." Ulm : Universität Ulm, 2021. http://d-nb.info/1237750725/34.

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Books on the topic "Robust Classification"

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J, Bees W., American Society of Mechanical Engineers. Pressure Vessels and Piping Division., and Pressure Vessels and Piping Conference (1993 : Denver, Colo.), eds. Design analysis, robust methods, and stress classification: Presented at the 1993 Pressure Vessels and Piping Conference, Denver, Colorado, July 25-29, 1993. American Society of Mechanical Engineers, 1993.

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C, Becht, Seshadri R, Marriott D. L, American Society of Mechanical Engineers. Pressure Vessels and Piping Division., and Pressure Vessels and Piping Conference (1992 : New Orleans, La.), eds. Stress classification, robust methods, and elevated temperature design: Presented at the 1992 Pressure Vessels and Piping Conference, New Orleans, Louisiana, June 21-25, 1992. American Society of Mechanical Engineers, 1992.

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NATO Advanced Research Workshop on Real-time Object and Environment Measurement and Classification (1987 Maratea, Italy). Real-time object measurement and classification. Springer-Verlag, 1988.

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Cayer, Micheline. Vocabulaire de la robotique: Classification et système mécanique. Gouvernement du Québec, Office de la langue française, 1993.

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Jain, Anil K. Real-Time Object Measurement and Classification. Springer Berlin Heidelberg, 1988.

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Healey, Anthony J. Sonar signal acquisition and processing for identification and classification of ship hull fouling. Naval Postgraduate School, 1993.

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Maxson, Linda R. Molecular systematics of the frog genus Leptodactylus (Amphibia : Leptodactylidae): A contribution in celebration of the distinguished scholarship of Robert F. Inger on the occasion of his sixty-fifth birthday. Field Museum of Natural History, 1988.

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Marx, Hymen. Phylogeny of the viperine snakes (Viperinae): A contribution in celebration of the distinguished scholarship of Robert F. Inger on the occasion of his sixty-fifth birthday. Field Museum of Natural History, 1988.

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Marx, Hymen. Phylogeny of the Viperine snakes (Viperinae): Part I. Character analysis : a contribution in celebration of the distinguished scholarship of Robert F. Inger on the occasion of his sixty-fifth birthday. Field Museum of Natural History, 1988.

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Sidney, Sheldon. Konet︠s︡ sveta. Novosti, 1995.

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Book chapters on the topic "Robust Classification"

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Abe, Shigeo. "Robust Function Approximation." In Pattern Classification. Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0285-4_16.

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Abe, Shigeo. "Robust Pattern Classification." In Pattern Classification. Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0285-4_8.

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Bini, Matilde, and Bruno Bertaccini. "Robust Fuzzy Classification." In Data Analysis and Classification. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03739-9_45.

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Sebastiani, Paola, and Marco Ramoni. "Robust Bayesian classification." In COMPSTAT. Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-642-57678-2_61.

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Atkinson, Anthony C., Marco Riani, and Andrea Cerioli. "Robust Clustering for Performance Evaluation." In Data Analysis and Classification. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03739-9_43.

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Alshemali, Basemah, Alta Graham, and Jugal Kalita. "Toward Robust Image Classification." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29513-4_35.

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Alzami, Farrikh, Daxing Wang, Zhiwen Yu, Jane You, Hau-San Wong, and Guoqiang Han. "Robust Epileptic Seizure Classification." In Intelligent Computing Theories and Application. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42294-7_32.

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Higger, Matt, Murat Akcakaya, Umut Orhan, and Deniz Erdogmus. "Robust Classification in RSVP Keyboard." In Foundations of Augmented Cognition. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39454-6_47.

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Liu, Fanghui, Xiaolin Huang, Cheng Peng, Jie Yang, and Nikola Kasabov. "Robust Kernel Approximation for Classification." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70087-8_31.

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Kim, Hyun-Chul, and Zoubin Ghahramani. "Outlier Robust Gaussian Process Classification." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89689-0_93.

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Conference papers on the topic "Robust Classification"

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Aoley, Rushikesh. "Robust Color Classification for Autonomous Robotic Boats." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724966.

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Sourati, Zhivar, Darshan Girish Deshpande, Filip Ilievski, Kiril Gashteovski, and Sascha Saralajew. "Robust Text Classification: Analyzing Prototype-Based Networks." In Findings of the Association for Computational Linguistics: EMNLP 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.745.

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Ge, Wenshu, Huan Ma, Peizhuo Sheng, and Changqing Zhang. "Uncertainty-aware Dynamic Re-weighting Robust Classification." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651411.

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Fu, Haoqi, Keqin Shi, and Weiqiang Sun. "Robust Classification of Step Data of Exercise." In 2019 IEEE 5th International Conference on Big Data Intelligence and Computing (DATACOM). IEEE, 2019. http://dx.doi.org/10.1109/datacom.2019.00022.

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Bhattarai, Sumit, Pramil Paudel, Zhu Li, Bo Luo, and Fengjun Li. "Robust Privacy-Preserving Classification for Lensless Images." In 2024 IEEE 21st International Conference on Mobile Ad-Hoc and Smart Systems (MASS). IEEE, 2024. http://dx.doi.org/10.1109/mass62177.2024.00056.

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Tucker, Andrew, and Steven Kay. "Robust spectral classification." In Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII, edited by Ivan Kadar. SPIE, 2018. http://dx.doi.org/10.1117/12.2304616.

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PARSONS, NH. "A ROBUST ALGORITHM FOR REVERBERATION SUPPRESSION IN DOPPLER SENSITIVE TRANSMISSIONS." In DETECTION & CLASSIFICATION OF UNDERWATER TARGETS 2007. Institute of Acoustics, 2023. http://dx.doi.org/10.25144/17802.

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Fidler, Sanja, and Ales Leonardis. "Robust LDA Classification by Subsampling." In 2003 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW). IEEE, 2003. http://dx.doi.org/10.1109/cvprw.2003.10089.

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Rattani, Ajita, D. R. Kisku, Manuele Bicego, and Massimo Tistarelli. "Robust Feature-Level Multibiometric Classification." In 2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference. IEEE, 2006. http://dx.doi.org/10.1109/bcc.2006.4341631.

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Shi, Shengli, Zhong Qin, and Jianmin Xu. "Robust Algorithm of Vehicle Classification." In Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007). IEEE, 2007. http://dx.doi.org/10.1109/snpd.2007.79.

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Reports on the topic "Robust Classification"

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Chowdhury, Shwetadwip. Automated extraction of cellular features for a potentially robust classification scheme. National Institute of Standards and Technology, 2010. http://dx.doi.org/10.6028/nist.ir.7738.

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Becker, Sarah, Craig Daughtry, and Andrew Russ. Robust forest cover indices for multispectral images. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42760.

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Trees occur in many land cover classes and provide significant ecosystem services. Remotely sensed multispectral images are often used to create thematic maps of land cover, but accurately identifying trees in mixed land-use scenes is challenging. We developed two forest cover indices and protocols that reliably identified trees in WorldView-2 multispectral images. The study site in Maryland included coniferous and deciduous trees associated with agricultural fields and pastures, residential and commercial buildings, roads, parking lots, wetlands, and forests. The forest cover indices exploited the product of either the reflectance in red (630 to 690 nm) and red edge (705 to 745 nm) bands or the product of reflectance in red and near infrared (770 to 895 nm) bands. For two classes (trees versus other), overall classification accuracy was &gt;77 percent for the four images that were acquired in each season of the year. Additional research is required to evaluate these indices for other scenes and sensors.
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Schnitta-Israel, B. Robust Detection and Classification of Regional Seismic Signals Using a Two Mode/Two Stage Cascaded Adaptive Arma (CAARMA) Model. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada154710.

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Boopalan, Santhana. Aerial Wildlife Image Repository. Mississippi State University, 2023. http://dx.doi.org/10.54718/wvgf3020.

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The availability of an ever-improving repository of datasets allows machine learning algorithms to have a robust training set of images, which in turn allows for accurate detection and classification of wildlife. This repository (AWIR---Aerial Wildlife Image Repository) would be a step in creating a collaborative rich dataset both in terms of taxa of animals and in terms of the sensors used to observe (visible, infrared, Lidar etc.). Initially, priority would be given to wildlife species hazardous to aircrafts, and common wildlife damage-associated species. AWIR dataset is accompanied by a classification benchmarking website showcasing examples of state-of-the-art algorithms recognizing the wildlife in the images.
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Maldonado, Leonardo, and Víctor Olivo. Is Venezuela Still an Upper-Middle-Income Country? Estimating the GNI per Capita for 2015–2021. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0004612.

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In the 2022 World Bank (WB) country classifications by income level, Venezuela is classified as an upper-middle-income country. Due to the lack of reliable official economic information from the Venezuelan regime, the WB ranked the country using its gross national income (GNI) of 2013. However, after 2013 Venezuela started to experience one of the largest economic collapses observed in Latin American history. We use three different approaches (the Atlas method, extrapolation, and an adjusted deflator) to obtain consistent and robust estimates of the GNI per capita for Venezuela up to 2021. Our findings reveal that Venezuela has been a lower-middle-income country since 2018 and suggest a 2021 GNI per capita of US$ 1,826 using the Atlas method, US$ 2,070 applying an extrapolation technique, and US$ 2,079 following an adjusted deflator. These results are substantially lower than the US$ 11,970 and US$ 13,080 reported by the WB for 2013 and 2014, respectively. A reconsideration of Venezuela's WB income-level classification could facilitate access to concessional conditions to internationally supported mechanisms.
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Fee, Kyle D. Income Inequality and Economic Growth in United States Counties: 1990s, 2000s and 2010s. Federal Reserve Bank of Cleveland, 2025. https://doi.org/10.26509/frbc-wp-202505.

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Using a common reduced-form regional growth model framework, an expanded geographic classification of counties, additional years of data, a trio of income inequality metrics, and multiple empirical specifications, this analysis confirms and builds upon the notion that the nature of the relationship between income inequality and economic growth varies across geography (Fallah and Partridge, 2007). A positive relationship between an income Gini coefficient and per capita income growth is observed only in central metro counties with population densities greater than 915 people per square mile or in about 5 percent of all counties, whereas previous research found a positive relationship in all metropolitan counties (27 percent of counties) and a negative relationship in nonmetropolitan counties. Where inequality is in the distribution is also shown to impact this relationship. Inequality in the top and bottom halves of the income distribution has a positive relationship with growth within this 5 percent of counties. However, in most locations (the other 95 percent of the counties), inequality in the bottom half of the income distribution has either no statistical relationship with growth or a positive relationship, while inequality in the top half of the income distribution tends to have a negative relationship. These patterns are relatively stable over time but tend to not be robust to the inclusion of county fixed effects. These results provide some evidence that the mechanisms explaining how this relationship varies across places are more likely associated with agglomeration and market incentives rather than social cohesion. This analysis also highlights the need for a robust research agenda focused on further refining the growth model along with incorporating new data sources and concepts of income inequality.
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Asari, Vijayan, Paheding Sidike, Binu Nair, Saibabu Arigela, Varun Santhaseelan, and Chen Cui. PR-433-133700-R01 Pipeline Right-of-Way Automated Threat Detection by Advanced Image Analysis. Pipeline Research Council International, Inc. (PRCI), 2015. http://dx.doi.org/10.55274/r0010891.

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A novel algorithmic framework for the robust detection and classification of machinery threats and other potentially harmful objects intruding onto a pipeline right-of-way (ROW) is designed from three perspectives: visibility improvement, context-based segmentation, and object recognition/classification. In the first part of the framework, an adaptive image enhancement algorithm is utilized to improve the visibility of aerial imagery to aid in threat detection. In this technique, a nonlinear transfer function is developed to enhance the processing of aerial imagery with extremely non-uniform lighting conditions. In the second part of the framework, the context-based segmentation is developed to eliminate regions from imagery that are not considered to be a threat to the pipeline. Context based segmentation makes use of a cascade of pre-trained classifiers to search for regions that are not threats. The context based segmentation algorithm accelerates threat identification and improves object detection rates. The last phase of the framework is an efficient object detection model. Efficient object detection �follows a three-stage approach which includes extraction of the local phase in the image and the use of local phase characteristics to locate machinery threats. The local phase is an image feature extraction technique which partially removes the lighting variance and preserves the edge information of the object. Multiple orientations of the same object are matched and the correct orientation is selected using feature matching by histogram of local phase in a multi-scale framework. The classifier outputs locations of threats to pipeline.�The advanced automatic image analysis system is intended to be capable of detecting construction equipment along the ROW of pipelines with a very high degree of accuracy in comparison with manual threat identification by a human analyst. �
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Wilson, D., Steven Peckham, Max Krackow, Sora Haley, Sophia Bragdon, and Jay Clausen. Discriminating buried munitions based on physical models for their thermal response. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49749.

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Munitions and other objects buried near the Earth’s surface can often be recognized in infrared imagery because their thermal and radiative properties differ from the surrounding undisturbed soil. However, the evolution of the thermal signature over time is subject to many complex interacting processes, including incident solar radiation, heat conduction in the ground, longwave radiation from the surface, and sensible and latent heat exchanges with the atmosphere. This complexity makes development of robust classification algorithms particularly challenging. Machine-learning algorithms, although increasingly popular, often require large training datasets including all environments to which they will be applied. Algorithms incorporating an understanding of the physical processes underlying the thermal signature potentially provide improved performance and mitigate the need for large training datasets. To that end, this report formulates a simplified model for the energy exchange near the ground and describes how it can be incorporated into maximum-likelihood ratio and Bayesian classifiers capable of distinguishing buried objects from their surroundings. In particular, a version of the Bayesian classifier is formulated that leverages the differing amplitude and phase response of a buried object over a 24-hour period. These algorithms will be tested on experimental data in a future study.
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Arnold, Joshua. DTPH56-16-T-00004 EMAT Guided Wave Technology for Inline Inspections of Unpiggable Natural Gas Pipelines. Pipeline Research Council International, Inc. (PRCI), 2018. http://dx.doi.org/10.55274/r0012048.

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This project developed compact, lightweight, prototype Electro-Magnetic Acoustic Transducers (EMATs) and studied guided waves for defect detection, classification, and characterization in cast iron and steel pipes. Through lab testing, design, and Finite Element Analysis (FEA), guided wave propagation and defect interactions were evaluated, and the results were employed to optimize the prototype EMATs through successive design and testing iterations. The goal of developing EMATs for robotic inspection of unpiggable pipe was successfully achieved and demonstrated not only through prototype fabrication and testing but also through conceptual design modifications to ULC's CIRRIS XITM robot that incorporated EMATs onto the robot.
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Бережна, Маргарита Василівна. Maleficent: from the Matriarch to the Scorned Woman (Psycholinguistic Image). Baltija Publishing, 2021. http://dx.doi.org/10.31812/123456789/5766.

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The aim of the research is to identify the elements of the psycholinguistic image of the leading character in the dark fantasy adventure film Maleficent directed by Robert Stromberg (2014). The task consists of two stages, at the first of which I identify the psychological characteristics of the character to determine to which of the archetypes Maleficent belongs. As the basis, I take the classification of film archetypes by V. Schmidt. At the second stage, I distinguish the speech peculiarities of the character that reflex her psychological image. This paper explores 98 Maleficent’s turns of dialogues in the film. According to V. Schmidt’s classification, Maleficent belongs first to the Matriarch archetype and later in the plot to the Scorned Woman archetype. These archetypes are representations of the powerful goddess of marriage and fertility Hera, being respectively her heroic and villainous embodiments. There are several crucial characteristics revealed by speech elements.
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