Relevant bibliographies by topics / Generative classifiers

Contents

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

Academic literature on the topic 'Generative classifiers'

Author: Grafiati
Published: 11 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "Generative classifiers"

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Varga, Michal, Ján Jadlovský, and Slávka Jadlovská. "Generative Enhancement of 3D Image Classifiers." Applied Sciences 10, no. 21 (2020): 7433. http://dx.doi.org/10.3390/app10217433.

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In this paper, we propose a methodology for generative enhancement of existing 3D image classifiers. This methodology is based on combining the advantages of both non-generative classifiers and generative modeling. Its purpose is to streamline the synthesis of novel deep neural networks by embedding existing compatible classifiers into a generative network architecture. A demonstration of this process and evaluation of its effectiveness is performed using a 3D convolutional classifier and its generative equivalent—a 3D conditional generative adversarial network classifier. The results of the e
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Zervou, Michaela, Effrosyni Doutsi, Yannis Pantazis, and Panagiotis Tsakalides. "De Novo Antimicrobial Peptide Design with Feedback Generative Adversarial Networks." International Journal of Molecular Sciences 25, no. 10 (2024): 5506. http://dx.doi.org/10.3390/ijms25105506.

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Antimicrobial peptides (AMPs) are promising candidates for new antibiotics due to their broad-spectrum activity against pathogens and reduced susceptibility to resistance development. Deep-learning techniques, such as deep generative models, offer a promising avenue to expedite the discovery and optimization of AMPs. A remarkable example is the Feedback Generative Adversarial Network (FBGAN), a deep generative model that incorporates a classifier during its training phase. Our study aims to explore the impact of enhanced classifiers on the generative capabilities of FBGAN. To this end, we intr
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Shakhuro, V. I., and A. S. Konushin. "IMAGE SYNTHESIS WITH NEURAL NETWORKS FOR TRAFFIC SIGN CLASSIFICATION." Computer Optics 42, no. 1 (2018): 105–12. http://dx.doi.org/10.18287/2412-6179-2018-42-1-105-112.

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In this work, we research the applicability of generative adversarial neural networks for generating training samples for a traffic sign classification task. We consider generative neural networks trained using the Wasserstein metric. As a baseline method for comparison, we take image generation based on traffic sign icons. Experimental evaluation of the classifiers based on convolutional neural networks is conducted on real data, two types of synthetic data, and a combination of real and synthetic data. The experiments show that modern generative neural networks are capable of generating real
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Hassan, Anthony Rotimi, Rasaki Olawale Olanrewaju, Queensley C. Chukwudum, Sodiq Adejare Olanrewaju, and S. E. Fadugba. "Comparison Study of Generative and Discriminative Models for Classification of Classifiers." International Journal of Mathematics and Computers in Simulation 16 (June 28, 2022): 76–87. http://dx.doi.org/10.46300/9102.2022.16.12.

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In classification of classifier analysis, researchers have been worried about the classifier of existing generative and discriminative models in practice for analyzing attributes data. This makes it necessary to give an in-depth, systematic, interrelated, interconnected, and classification of classifier of generative and discriminative models. Generative models of Logistic and Multinomial Logistic regression models and discriminative models of Linear Discriminant Analysis (LDA) (for attribute P=1 and P>1), Quadratic Discriminant Analysis (QDA) and Naïve Bayes were thoroughly dealt with anal
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Joo, Jaehan, Sang Yoon Kim, Donghwan Kim, et al. "Enhancing automated strabismus classification with limited data: Data augmentation using StyleGAN2-ADA." PLOS ONE 19, no. 5 (2024): e0303355. http://dx.doi.org/10.1371/journal.pone.0303355.

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In this study, we propose a generative data augmentation technique to overcome the challenges of severely limited data when designing a deep learning-based automated strabismus diagnosis system. We implement a generative model based on the StyleGAN2-ADA model for system design and assess strabismus classification performance using two classifiers. We evaluate the capability of our proposed method against traditional data augmentation techniques and confirm a substantial enhancement in performance. Furthermore, we conduct experiments to explore the relationship between the diagnosis agreement a
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Anil, Gautham, Vishnu Vinod, and Apurva Narayan. "Generating Universal Adversarial Perturbations for Quantum Classifiers." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (2024): 10891–99. http://dx.doi.org/10.1609/aaai.v38i10.28963.

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Quantum Machine Learning (QML) has emerged as a promising field of research, aiming to leverage the capabilities of quantum computing to enhance existing machine learning methodologies. Recent studies have revealed that, like their classical counterparts, QML models based on Parametrized Quantum Circuits (PQCs) are also vulnerable to adversarial attacks. Moreover, the existence of Universal Adversarial Perturbations (UAPs) in the quantum domain has been demonstrated theoretically in the context of quantum classifiers. In this work, we introduce QuGAP: a novel framework for generating UAPs for
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Abady, Lydia, Giovanna Maria Dimitri, and Mauro Barni. "A One-Class Classifier for the Detection of GAN Manipulated Multi-Spectral Satellite Images." Remote Sensing 16, no. 5 (2024): 781. http://dx.doi.org/10.3390/rs16050781.

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The current image generative models have achieved a remarkably realistic image quality, offering numerous academic and industrial applications. However, to ensure these models are used for benign purposes, it is essential to develop tools that definitively detect whether an image has been synthetically generated. Consequently, several detectors with excellent performance in computer vision applications have been developed. However, these detectors cannot be directly applied as they areto multi-spectral satellite images, necessitating the training of new models. While two-class classifiers gene
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Kumar Bhowmik, Tapan. "Naive Bayes vs Logistic Regression: Theory, Implementation and Experimental Validation." Inteligencia Artificial 18, no. 56 (2015): 14. http://dx.doi.org/10.4114/intartif.vol18iss56pp14-30.

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This article presents the theoretical derivation as well as practical steps for implementing Naive Bayes (NB) and Logistic Regression (LR) classifiers. A generative learning under Gaussian Naive Bayes assumption and two discriminative learning techniques based on gradient ascent and Newton-Raphson methods are described to estimate the parameters of LR. Some limitation of learning techniques and implementation issues are discussed as well. A set of experiments are performed for both the classifiers under different learning circumstances and their performances are compared. From the experiments,
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Lu, Zhengdong, Todd K. Leen, and Jeffrey Kaye. "Kernels for Longitudinal Data with Variable Sequence Length and Sampling Intervals." Neural Computation 23, no. 9 (2011): 2390–420. http://dx.doi.org/10.1162/neco_a_00164.

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We develop several kernel methods for classification of longitudinal data and apply them to detect cognitive decline in the elderly. We first develop mixed-effects models, a type of hierarchical empirical Bayes generative models, for the time series. After demonstrating their utility in likelihood ratio classifiers (and the improvement over standard regression models for such classifiers), we develop novel Fisher kernels based on mixture of mixed-effects models and use them in support vector machine classifiers. The hierarchical generative model allows us to handle variations in sequence lengt
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Sensoy, Murat, Lance Kaplan, Federico Cerutti, and Maryam Saleki. "Uncertainty-Aware Deep Classifiers Using Generative Models." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5620–27. http://dx.doi.org/10.1609/aaai.v34i04.6015.

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Deep neural networks are often ignorant about what they do not know and overconfident when they make uninformed predictions. Some recent approaches quantify classification uncertainty directly by training the model to output high uncertainty for the data samples close to class boundaries or from the outside of the training distribution. These approaches use an auxiliary data set during training to represent out-of-distribution samples. However, selection or creation of such an auxiliary data set is non-trivial, especially for high dimensional data such as images. In this work we develop a nove
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Dissertations / Theses on the topic "Generative classifiers"

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Xue, Jinghao. "Aspects of generative and discriminative classifiers." Thesis, Connect to e-thesis, 2008. http://theses.gla.ac.uk/272/.

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Thesis (Ph.D.) - University of Glasgow, 2008.<br>Ph.D. thesis submitted to the Department of Statistics, Faculty of Information and Mathematical Sciences, University of Glasgow, 2008. Includes bibliographical references. Print version also available.
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Chali, Samy. "Robustness Analysis of Classifiers Against Out-of-Distribution and Adversarial Inputs." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPAST012.

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De nombreux problèmes traités par l'IA sont des problèmes de classification de données d'entrées complexes qui doivent être séparées en différentes classes. Les fonctions transformant l'espace complexe des valeurs d'entrées en un espace plus simple, linéairement séparable, se font soit par apprentissage (réseaux convolutionels profonds), soit par projection dans un espace de haute dimension afin d'obtenir une représentation non-linéaire 'riche' des entrées puis un appariement linaire entre l'espace de haute dimension et les unités de sortie, tels qu'utilisés dans les Support Vector Machines (t
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ROGER-YUN, Soyoung. "Les expressions nominales à classificateurs et les propositions à cas multiples du coréen : recherches sur leur syntaxe interne et mise en évidence de quelques convergences structurales." Phd thesis, Université de la Sorbonne nouvelle - Paris III, 2002. http://tel.archives-ouvertes.fr/tel-00002834.

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Cette thèse a pour objet la syntaxe des classificateurs (CL) et des Constructions à Cas Multiples du coréen. Cette étude adopte essentiellement le cadre antisymétrique de Kayne, mais utilise également certains concepts fondamentaux du cadre minimaliste, comme la Vérification des traits formels. La première partie de cette thèse est consacrée à l'étude des CL et de la structure interne des expressions nominales à CL; nous montrons notamment qu'un traitement syntaxique parallèle pour les domaines nominal et phrastique est possible en coréen. Dans la seconde partie, consacrée à la structure phras
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McClintick, Kyle W. "Training Data Generation Framework For Machine-Learning Based Classifiers." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1276.

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In this thesis, we propose a new framework for the generation of training data for machine learning techniques used for classification in communications applications. Machine learning-based signal classifiers do not generalize well when training data does not describe the underlying probability distribution of real signals. The simplest way to accomplish statistical similarity between training and testing data is to synthesize training data passed through a permutation of plausible forms of noise. To accomplish this, a framework is proposed that implements arbitrary channel conditions and base
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Guo, Hong Yu. "Multiple classifier combination through ensembles and data generation." Thesis, University of Ottawa (Canada), 2004. http://hdl.handle.net/10393/26648.

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This thesis introduces new approaches, namely the DataBoost and DataBoost-IM algorithms, to extend Boosting algorithms' predictive performance. The DataBoost algorithm is designed to assist Boosting algorithms to avoid over-emphasizing hard examples. In the DataBoost algorithm, new synthetic data with bias information towards hard examples are added to the original training set when training the component classifiers. The DataBoost approach was evaluated against ten data sets, using both decision trees and neural networks as base classifiers. The experiments show promising results, in terms of
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Kang, Dae-Ki. "Abstraction, aggregation and recursion for generating accurate and simple classifiers." [Ames, Iowa : Iowa State University], 2006.

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Kimura, Takayuki. "RNA-protein structure classifiers incorporated into second-generation statistical potentials." Thesis, San Jose State University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10241445.

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<p> Computational modeling of RNA-protein interactions remains an important endeavor. However, exclusively all-atom approaches that model RNA-protein interactions via molecular dynamics are often problematic in their application. One possible alternative is the implementation of hierarchical approaches, first efficiently exploring configurational space with a coarse-grained representation of the RNA and protein. Subsequently, the lowest energy set of such coarse-grained models can be used as scaffolds for all-atom placements, a standard method in modeling protein 3D-structure. However, the coa
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Alani, Shayma. "Design of intelligent ensembled classifiers combination methods." Thesis, Brunel University, 2015. http://bura.brunel.ac.uk/handle/2438/12793.

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Classifier ensembling research has been one of the most active areas of machine learning for a long period of time. The main aim of generating combined classifier ensembles is to improve the prediction accuracy in comparison to using an individual classifier. A combined classifiers ensemble can improve the prediction results by compensating for the individual classifier weaknesses in certain areas and benefiting from better accuracy of the other ensembles in the same area. In this thesis, different algorithms are proposed for designing classifier ensemble combiners. The existing methods such a
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DING, ZEJIN. "Diversified Ensemble Classifiers for Highly Imbalanced Data Learning and their Application in Bioinformatics." Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/cs_diss/60.

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In this dissertation, the problem of learning from highly imbalanced data is studied. Imbalance data learning is of great importance and challenge in many real applications. Dealing with a minority class normally needs new concepts, observations and solutions in order to fully understand the underlying complicated models. We try to systematically review and solve this special learning task in this dissertation.We propose a new ensemble learning framework—Diversified Ensemble Classifiers for Imbal-anced Data Learning (DECIDL), based on the advantages of existing ensemble imbalanced learning str
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Svénsen, Johan F. M. "GTM: the generative topographic mapping." Thesis, Aston University, 1998. http://publications.aston.ac.uk/1245/.

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This thesis describes the Generative Topographic Mapping (GTM) --- a non-linear latent variable model, intended for modelling continuous, intrinsically low-dimensional probability distributions, embedded in high-dimensional spaces. It can be seen as a non-linear form of principal component analysis or factor analysis. It also provides a principled alternative to the self-organizing map --- a widely established neural network model for unsupervised learning --- resolving many of its associated theoretical problems. An important, potential application of the GTM is visualization of high-dimensio
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Books on the topic "Generative classifiers"

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Bondarenko, Natal'ya. Pattern recognition. The initial course of theory. INFRA-M Academic Publishing LLC., 2024. http://dx.doi.org/10.12737/2111834.

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This tutorial discusses the tasks of pattern recognition, discriminant analysis, taxonomy, comparison with a reference, classification of features, and selection of a feature space. The main groups of features calculated from images and used for their recognition have been studied. The methods of classification based on comparison with the standard, the Bayesian classifier and decision trees are highlighted. Meets the requirements of the federal state educational standards of higher education of the latest generation. For students studying in the field of information technology, applied mathem
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Martín-Vide, Carlos. Formal Grammars and Languages. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0008.

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This article introduces the preliminaries of classical formal language theory. It outlines the main classes of grammars as language-generating devices and automata as language-recognizing devices. It offers a number of definitions and examples and presents the basic results. It classifies grammar according to several criteria. The most widespread one is the form of their productions. This article presents a systematic study of the common properties of language families has led to the theory of abstract families of languages. It shows that a context-free grammar generates not only a set of stri
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Abbas, Atheir I., and Jeffrey A. Lieberman. Pharmacological Treatments for Schizophrenia. Oxford University Press, 2015. http://dx.doi.org/10.1093/med:psych/9780199342211.003.0006.

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Schizophrenia, a chronic mental disorder, has a lifetime prevalence rate of approximately 1%. The first antipsychotic drug, chlorpromazine, was introduced in 1954, followed by several similar drugs. With the introduction of clozapine, risperidone, olanzapine, quetiapine, ziprasidone, aripiprazole, and more recently paliperidone, iloperidone, asenapine, and lurasidone, antipsychotic drugs are often classified as first generation or typical (chlorpromazine-like) versus second generation or atypical (clozapine-like), although the distinction between the two classes, particularly with respect to e
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Solms, Mark. Sleep and dreams. Edited by Sudhansu Chokroverty, Luigi Ferini-Strambi, and Christopher Kennard. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199682003.003.0034.

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Despite the minimal attention that physicians typically pay to dreams, the assessment of dreaming can be of diagnostic interest and have management implications. This chapter reviews the world literature on dream abnormalities of clinical neurological significance, starting with the classical concept of the Charcot–Wilbrand syndrome (anoneira). This and the other recognized disorders are broadly classified here under headings of “deficits” and “excesses” of dreaming. Also reviewed are major trends in the neuroimaging and neurophysiological literature regarding dreams and their relationship to
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Ferguson, Ben, and Hillel Steiner. Exploitation. Edited by Serena Olsaretti. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199645121.013.21.

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Exploitation is commonly understood as taking unfair advantage. This article discusses the various prominent accounts that have been offered of how an exchange, despite being Pareto improving and consensual, can nevertheless count as unfair or unjust and, hence, as presumptively impermissible. Does the wrongness of an exploitative transaction consist in its compounding a prior distributive injustice, or in its deliberately profiting from someone’s vulnerability, or in its commodification of that which should not be commodified? How should responsibility for exploitation be assigned, and can th
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Biddle, Justin B., and Rebecca Kukla. The Geography of Epistemic Risk. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190467715.003.0011.

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At each stage of inquiry, actions, choices, and judgments carry with them a chance that they will lead to mistakes and false conclusions. One of the most vigorously discussed kinds of epistemic risk is inductive risk—that is, the risk of inferring a false positive or a false negative from statistical evidence. This chapter develops a more fine-grained typology of epistemic risks and argues that many of the epistemic risks that have been classified as inductive risks are actually better seen as examples of a more expansive category, which this paper dubs “phronetic risk.” This more fine-grained
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Lalvani, Ajit, and Katrina Pollock. Defences against infection. Edited by Patrick Davey and David Sprigings. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199568741.003.0303.

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The immune system is classified into a series of component parts, each specialized to defend the host against infection. Cells of the innate immune system are distributed throughout the body, in the tissues, and in the circulation, to defend against the first signs of danger, combining the acute inflammatory response with the ability to kill and remove invading pathogens. Monocytes, macrophages, and neutrophils phagocytose and kill exogenous and endogenous targets, using both oxygen-dependent and oxygen-independent mechanisms. The adaptive immune system creates a structurally specific and prol
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Caramello, Olivia. Theories of presheaf type: general criteria. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198758914.003.0008.

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This chapter carries out a systematic investigation of the class of geometric theories of presheaf type (i.e. classified by a presheaf topos), by using in particular the results on flat functors established in Chapter 5. First, it establishes a number of general results on theories of presheaf type, notably including a definability theorem and a characterization of the finitely presentable models of such a theory in terms of formulas satisfying a key property of irreducibility. Then it presents a fully constructive characterization theorem providing necessary and sufficient conditions for a th
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Book chapters on the topic "Generative classifiers"

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Yang, Xiulong, Hui Ye, Yang Ye, Xiang Li, and Shihao Ji. "Generative Max-Mahalanobis Classifiers for Image Classification, Generation and More." In Machine Learning and Knowledge Discovery in Databases. Research Track. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86520-7_5.

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Wang, Yaxiao, Yuanzhang Li, Quanxin Zhang, Jingjing Hu, and Xiaohui Kuang. "Evading PDF Malware Classifiers with Generative Adversarial Network." In Cyberspace Safety and Security. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37337-5_30.

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Drummond, Chris. "Discriminative vs. Generative Classifiers for Cost Sensitive Learning." In Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11766247_41.

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Santafé, Guzmán, Jose A. Lozano, and Pedro Larrañaga. "Discriminative vs. Generative Learning of Bayesian Network Classifiers." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75256-1_41.

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Tran, Quang Duy, and Fabio Di Troia. "Word Embeddings for Fake Malware Generation." In Silicon Valley Cybersecurity Conference. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-24049-2_2.

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AbstractSignature and anomaly-based techniques are the fundamental methods to detect malware. However, in recent years this type of threat has advanced to become more complex and sophisticated, making these techniques less effective. For this reason, researchers have resorted to state-of-the-art machine learning techniques to combat the threat of information security. Nevertheless, despite the integration of the machine learning models, there is still a shortage of data in training that prevents these models from performing at their peak. In the past, generative models have been found to be hi
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Antoniou, Antreas, Amos Storkey, and Harrison Edwards. "Augmenting Image Classifiers Using Data Augmentation Generative Adversarial Networks." In Artificial Neural Networks and Machine Learning – ICANN 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01424-7_58.

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Agarwal, Chirag, and Anh Nguyen. "Explaining Image Classifiers by Removing Input Features Using Generative Models." In Computer Vision – ACCV 2020. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69544-6_7.

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Carrasco Limeros, Sandra, Sylwia Majchrowska, Mohamad Khir Zoubi, et al. "Assessing GAN-Based Generative Modeling on Skin Lesions Images." In Digital Interaction and Machine Intelligence. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37649-8_10.

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AbstractWe explored unconditional and conditional Generative Adversarial Networks (GANs) in centralized and decentralized settings. The centralized setting imitates studies on large but highly unbalanced skin lesion dataset, while the decentralized one simulates a more realistic hospital scenario with three institutions. We evaluated models’ performance in terms of fidelity, diversity, speed of training, and predictive ability of classifiers trained on the generated synthetic data. In addition, we provided explainability focused on both global and local features. Calculated distance between re
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Pingi, Sharon Torao, Md Abul Bashar, and Richi Nayak. "A Comparative Look at the Resilience of Discriminative and Generative Classifiers to Missing Data in Longitudinal Datasets." In Communications in Computer and Information Science. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-8746-5_10.

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Zanda, Manuela, and Gavin Brown. "A Study of Semi-supervised Generative Ensembles." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02326-2_25.

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Conference papers on the topic "Generative classifiers"

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Boum, Marie-Ange, Stéphane Herbin, Pierre Fournier, and Pierre Lassalle. "Continual Learning in Remote Sensing : Leveraging Foundation Models and Generative Classifiers to Mitigate Forgetting." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10640659.

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Roy, Bipraneel, Hon Cheung, and Chun Ruan. "Intrusion Classifier Architecture Generation with Zero Prior Knowledge Employing Generative Adversarial Networks." In 2024 IEEE International Conference on Future Machine Learning and Data Science (FMLDS). IEEE, 2024. https://doi.org/10.1109/fmlds63805.2024.00016.

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Ballesteros, Miguel, Simon Mille, and Leo Wanner. "Classifiers for data-driven deep sentence generation." In Proceedings of the 8th International Natural Language Generation Conference (INLG). Association for Computational Linguistics, 2014. https://doi.org/10.18653/v1/w14-4416.

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Sathan, Dassen, and Shakuntala Baichoo. "Drug Target Interaction prediction using Variational Quantum classifier." In 2024 International Conference on Next Generation Computing Applications (NextComp). IEEE, 2024. https://doi.org/10.1109/nextcomp63004.2024.10779674.

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Hossain, Md Radowan, Shakiruzzaman, Gazi Jannatul Ferdous, and Md Azad Hossain. "BrainACGAN: Auxiliary Classifier Generative Adversarial Network for Brain Tumor Images." In 2024 International Conference on Innovations in Science, Engineering and Technology (ICISET). IEEE, 2024. https://doi.org/10.1109/iciset62123.2024.10939632.

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van de Ven, Gido M., Zhe Li, and Andreas S. Tolias. "Class-Incremental Learning with Generative Classifiers." In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2021. http://dx.doi.org/10.1109/cvprw53098.2021.00400.

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Smith, Andrew T., and Charles Elkan. "Making generative classifiers robust to selection bias." In the 13th ACM SIGKDD international conference. ACM Press, 2007. http://dx.doi.org/10.1145/1281192.1281263.

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Wang, Xin, and Siu Ming Yiu. "Classification with Rejection: Scaling Generative Classifiers with Supervised Deep Infomax." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/412.

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Deep Infomax (DIM) is an unsupervised representation learning framework by maximizing the mutual information between the inputs and the outputs of an encoder, while probabilistic constraints are imposed on the outputs. In this paper, we propose Supervised Deep InfoMax (SDIM), which introduces supervised probabilistic constraints to the encoder outputs. The supervised probabilistic constraints are equivalent to a generative classifier on high-level data representations, where class conditional log-likelihoods of samples can be evaluated. Unlike other works building generative classifiers with c
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Yoshida, Hidefumi, Daichi Suzuo, Daisuke Deguchi, et al. "Pedestrian detection by scene dependent classifiers with generative learning." In 2013 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2013. http://dx.doi.org/10.1109/ivs.2013.6629541.

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Shin, Donghwa, Daehee Han, and Sunghyon Kyeong. "Performance Enhancement of Malware Classifiers Using Generative Adversarial Networks." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020505.

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Reports on the topic "Generative classifiers"

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Mittal, Vibhu O., and Cecile L. Paris. Generating Examples for Use in Tutorial Explanations: The Use of a Subsumption Based Classifier. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada286028.

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Mbani, Benson, Timm Schoening, and Jens Greinert. Automated and Integrated Seafloor Classification Workflow (AI-SCW). GEOMAR, 2023. http://dx.doi.org/10.3289/sw_2_2023.

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The Automated and Integrated Seafloor Classification Workflow (AI-SCW) is a semi-automated underwater image processing pipeline that has been customized for use in classifying the seafloor into semantic habitat categories. The current implementation has been tested against a sequence of underwater images collected by the Ocean Floor Observation System (OFOS), in the Clarion-Clipperton Zone of the Pacific Ocean. Despite this, the workflow could also be applied to images acquired by other platforms such as an Autonomous Underwater Vehicle (AUV), or Remotely Operated Vehicle (ROV). The modules in
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Lasko, Kristofer, Francis O’Neill, and Elena Sava. Automated mapping of land cover type within international heterogenous landscapes using Sentinel-2 imagery with ancillary geospatial data. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49367.

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A near-global framework for automated training data generation and land cover classification using shallow machine learning with low-density time series imagery does not exist. This study presents a methodology to map nine-class, six-class, and five-class land cover using two dates of a Sentinel-2 granule across seven international sites. The approach uses a series of spectral, textural, and distance decision functions combined with modified ancillary layers to create binary masks from which to generate a balanced set of training data applied to a random forest classifier. For the land cover m
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Bueso-Merriam, Jacqueline, Francisco Demichelis, María Carmen Fernández Díez, David Giuliodori, Alejandro Rodríguez, and Rodolfo Stucchi. The Impact of the Lending Program for the Productive Development and Employment Generation of the San Juan Province. Inter-American Development Bank, 2016. http://dx.doi.org/10.18235/0007975.

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A shortcoming of the current empirical impact evaluation literature is the lack of studies that measure the effect of access to credit in the medium and long term, as well as the impact on interventions targeted at micro-, small-, and medium-sized enterprises (MSMEs). This study addresses both aspects by analyzing the average impact that the Lending Program for the Productive Development and Employment Generation of the San Juan Province has had on sales, employment, investment, and productivity of beneficiary MSMEs. The authors measured the abovementioned impacts using a lagged dependent vari
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โขวิฑูรกิจ, วีรพันธุ์, พัชญา บุญชยาอนันต์ та มงคลธิดา อัมพลเสถียร. รายงานฉบับสมบูรณ์ แผนงานวิจัยเพื่อองค์ความรู้ใหม่ทางวิทยาศาสตร์พื้นฐานและการประยุกต์ใช้ทางคลินิกในคนไข้ที่มีภาวะโรคอ้วนและโรคเบาหวาน. จุฬาลงกรณ์มหาวิทยาลัย, 2017. https://doi.org/10.58837/chula.res.2017.12.

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โรคเบาหวานชนิดที่ 2 และโรคอ้วนถือเป็นปัญหาสุขภาพเรื้อรังที่สำคัญทำให้เกิดภาวะทุพพลภาพตามมา ความชุกของโรคเบาหวานและโรคอ้วนนั้นยังมีแนวโน้มเพิ่มมากขึ้นเรื่อยๆ ปัจจัยทางสิ่งแวดล้อม เช่น อาหารการกิน การออกกำลังกาย การใช้ชีวิตประจำวัน มีผลต่อความเสี่ยงในการเกิดโรคเบาหวานและโรคอ้วน แบคทีเรียในลำไส้เป็นตัวเชื่อมระหว่างร่างกายมนุษย์และสิ่งแวดล้อม มีหลักฐานว่าแบคทีเรียในลำไส้นี้มีผลต่อระบบเมตะบอลิสมในร่างกายและการเปลี่ยนแปลงของแบคทีเรียในลำไส้สัมพันธ์กับการเกิดโรคเบาหวานและโรคอ้วน ในการศึกษานี้ ทำการตรวจหาประชากรแบคทีเรียในลำไส้ด้วยวิธี 16S metagenomic based next-generation sequencing (NGS) จากตัวอย่าง
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Dzanku, Fred M., and Louis S. Hodey. Achieving Inclusive Oil Palm Commercialisation in Ghana. Institute of Development Studies (IDS), 2022. http://dx.doi.org/10.19088/apra.2022.007.

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Oil palm is the most important export crop in Ghana, aside from cocoa. Compared with cocoa, however, oil palm has a more extensive local value chain, including greater opportunity for local industrial and artisanal processing into palm oil and other products, which creates a high potential for employment generation and poverty reduction; as a result oil palm is classified as a priority crop. The selection of oil palm as a priority crop aims to promote agricultural commercialisation through domestic agroindustry development and exports. In spite of this, the oil palm economy has still not achie
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van den Boogaard, Vanessa, and Fabrizio Santoro. Explaining Informal Taxation and Revenue Generation: Evidence from south-central Somalia. Institute of Development Studies, 2021. http://dx.doi.org/10.19088/ictd.2021.003.

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Most people in low-income countries contribute substantially to the financing of local public goods through informal revenue generation (IRG). However, very little is known about how IRG works in practice. We produce novel evidence on the magnitude and regressivity of IRG and its relationship with the state in a fragile context, Somalia. We rely on original data from surveys with over 2,300 households and 117 community leaders in Gedo region, as well as on extensive qualitative research. We first show that IRG is prevalent. Over 70 per cent of households report paying at least one informal tax
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Bäumler, Maximilian, and Matthias Lehmann. Generating representative test scenarios: The FUSE for Representativity (fuse4rep) process model for collecting and analysing traffic observation data. TU Dresden, 2024. http://dx.doi.org/10.26128/2024.2.

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Scenario-based testing is a pillar of assessing the effectiveness of automated driving systems (ADSs). For data-driven scenario-based testing, representative traffic scenarios need to describe real road traffic situations in compressed form and, as such, cover normal driving along with critical and accident situations originating from different data sources. Nevertheless, in the choice of data sources, a conflict often arises between sample quality and depth of information. Police accident data (PD) covering accident situations, for example, represent a full survey and thus have high sample qu
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Rodriguez, Russell, and Stanley Freeman. Characterization of fungal symbiotic lifestyle expression in Colletotrichum and generating non-pathogenic mutants that confer disease resistance, drought tolerance, and growth enhancement to plant hosts. United States Department of Agriculture, 2005. http://dx.doi.org/10.32747/2005.7587215.bard.

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Fungal plant pathogens are responsible for extensive annual crop and revenue losses throughout the world. To better understand why fungi cause diseases, we performed gene-disruption mutagenesis on several pathogenic Colletotrichum species and demonstrated that pathogenic isolates can be converted to symbionts (mutualism, commensalism, parasitism) expressing non-pathogenic lifestyles. The objectives of this proposal were to: 1- generate crop-specific mutants by gene disruption that express mutualistic lifestyles, 2- assess the ability of the mutualists to confer disease resistance, drought tole
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Kwon, Heeseo Rain, HeeAh Cho, Jongbok Kim, Sang Keon Lee, and Donju Lee. International Case Studies of Smart Cities: Pangyo, Republic of Korea. Inter-American Development Bank, 2016. http://dx.doi.org/10.18235/0007011.

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This case study is one of ten international studies developed by the Korea Research Institute for Human Settlements (KRIHS), in association with the Inter-American Development Bank (IDB), for the cities of Anyang, Medellin, Namyangju, Orlando, Pangyo, Rio de Janeiro, Santander, Singapore, Songdo, and Tel Aviv. At the IDB, the Competitiveness and Innovation Division (CTI), the Fiscal and Municipal Management Division (FMM), and the Emerging and Sustainable Cities Initiative (ESCI) coordinated the study. This project was part of technical cooperation ME-T1254, financed by the Knowledge Partnersh
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