Relevant bibliographies by topics / Generative classifiers

Academic literature on the topic 'Generative classifiers'

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Published: 11 March 2023

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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 (October 22, 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 experiments show that the generative classifier delivers higher performance, gaining a relative classification accuracy improvement of 7.43%. An increase of accuracy is also observed when comparing it to a plain convolutional classifier that was trained on a dataset augmented with samples created by the trained generator. This suggests a desirable knowledge sharing mechanism exists within the hybrid discriminator-classifier network.
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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 (March 30, 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 realistic training samples for traffic sign classification that outperform methods for generating images with icons, but are still slightly worse than real images for classifier training.
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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 (April 3, 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 novel neural network model that is able to express both aleatoric and epistemic uncertainty to distinguish decision boundary and out-of-distribution regions of the feature space. To this end, variational autoencoders and generative adversarial networks are incorporated to automatically generate out-of-distribution exemplars for training. Through extensive analysis, we demonstrate that the proposed approach provides better estimates of uncertainty for in- and out-of-distribution samples, and adversarial examples on well-known data sets against state-of-the-art approaches including recent Bayesian approaches for neural networks and anomaly detection methods.
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Yakura, Hiromu, Youhei Akimoto, and Jun Sakuma. "Generate (Non-Software) Bugs to Fool Classifiers." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 1070–78. http://dx.doi.org/10.1609/aaai.v34i01.5457.

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In adversarial attacks intended to confound deep learning models, most studies have focused on limiting the magnitude of the modification so that humans do not notice the attack. On the other hand, during an attack against autonomous cars, for example, most drivers would not find it strange if a small insect image were placed on a stop sign, or they may overlook it. In this paper, we present a systematic approach to generate natural adversarial examples against classification models by employing such natural-appearing perturbations that imitate a certain object or signal. We first show the feasibility of this approach in an attack against an image classifier by employing generative adversarial networks that produce image patches that have the appearance of a natural object to fool the target model. We also introduce an algorithm to optimize placement of the perturbation in accordance with the input image, which makes the generation of adversarial examples fast and likely to succeed. Moreover, we experimentally show that the proposed approach can be extended to the audio domain, for example, to generate perturbations that sound like the chirping of birds to fool a speech classifier.
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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 analytically and mathematically. A step-by-step empirical analysis of the mentioned models were carried-out via chemical analysis of wines grown in a region in Italy that was derived from three different cultivars (The three types of wines that constituted the three different cultivars or three classifiers). Naïve Bayes Classifier set the pace via leading a-prior probabilities.
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Chen, Wei, Xinmiao Chen, and Xiao Sun. "Emotional dialog generation via multiple classifiers based on a generative adversarial network." Virtual Reality & Intelligent Hardware 3, no. 1 (February 2021): 18–32. http://dx.doi.org/10.1016/j.vrih.2020.12.001.

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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 (September 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 length and sampling interval gracefully. We also give nonparametric kernels not based on generative models, but rather on the reproducing kernel Hilbert space. We apply the methods to detecting cognitive decline from longitudinal clinical data on motor and neuropsychological tests. The likelihood ratio classifiers based on the neuropsychological tests perform better than than classifiers based on the motor behavior. Discriminant classifiers performed better than likelihood ratio classifiers for the motor behavior tests.
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Amiryousefi, Ali, Ville Kinnula, and Jing Tang. "Bayes in Wonderland! Predictive Supervised Classification Inference Hits Unpredictability." Mathematics 10, no. 5 (March 5, 2022): 828. http://dx.doi.org/10.3390/math10050828.

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The marginal Bayesian predictive classifiers (mBpc), as opposed to the simultaneous Bayesian predictive classifiers (sBpc), handle each data separately and, hence, tacitly assume the independence of the observations. Due to saturation in learning of generative model parameters, the adverse effect of this false assumption on the accuracy of mBpc tends to wear out in the face of an increasing amount of training data, guaranteeing the convergence of these two classifiers under the de Finetti type of exchangeability. This result, however, is far from trivial for the sequences generated under Partition Exchangeability (PE), where even umpteen amount of training data does not rule out the possibility of an unobserved outcome (Wonderland!). We provide a computational scheme that allows the generation of the sequences under PE. Based on that, with controlled increase of the training data, we show the convergence of the sBpc and mBpc. This underlies the use of simpler yet computationally more efficient marginal classifiers instead of simultaneous. We also provide a parameter estimation of the generative model giving rise to the partition exchangeable sequence as well as a testing paradigm for the equality of this parameter across different samples. The package for Bayesian predictive supervised classifications, parameter estimation and hypothesis testing of the Ewens sampling formula generative model is deposited on CRAN as PEkit package.
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Elzobi, Moftah, and Ayoub Al-Hamadi. "Generative vs. Discriminative Recognition Models for Off-Line Arabic Handwriting." Sensors 18, no. 9 (August 24, 2018): 2786. http://dx.doi.org/10.3390/s18092786.

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The majority of handwritten word recognition strategies are constructed on learning-based generative frameworks from letter or word training samples. Theoretically, constructing recognition models through discriminative learning should be the more effective alternative. The primary goal of this research is to compare the performances of discriminative and generative recognition strategies, which are described by generatively-trained hidden Markov modeling (HMM), discriminatively-trained conditional random fields (CRF) and discriminatively-trained hidden-state CRF (HCRF). With learning samples obtained from two dissimilar databases, we initially trained and applied an HMM classification scheme. To enable HMM classifiers to effectively reject incorrect and out-of-vocabulary segmentation, we enhance the models with adaptive threshold schemes. Aside from proposing such schemes for HMM classifiers, this research introduces CRF and HCRF classifiers in the recognition of offline Arabic handwritten words. Furthermore, the efficiencies of all three strategies are fully assessed using two dissimilar databases. Recognition outcomes for both words and letters are presented, with the pros and cons of each strategy emphasized.
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Karaliutė, Marta, and Kęstutis Dučinskas. "Performance of the supervised generative classifiers of spatio-temporal areal data using various spatial autocorrelation indexes." Nonlinear Analysis: Modelling and Control 28 (February 22, 2023): 1–14. http://dx.doi.org/10.15388/namc.2023.28.31434.

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This article is concerned with a generative approach to supervised classification of spatio-temporal data collected at fixed areal units and modeled by Gaussian Markov random field. We focused on the classifiers based on Bayes discriminant functions formed by the log-ratio of the class conditional likelihoods. As a novel modeling contribution, we propose to use decision threshold values induced by three popular spatial autocorrelation indexes, i.e., Moran’s I, Geary’s C and Getis–Ord G. The goal of this study is to extend the recent investigations in the context of geostatistical and hidden Markov Gaussian models to one in the context of areal Gaussian Markov models. The classifiers performance measures are chosen to be the average accuracy rate, which shows the percentage of correctly classified test data, balanced accuracy rate specified by the average of sensitivity and specificity and the geometric mean of sensitivity and specificity. The proposed methodology is illustrated using annual death rate data collected by the Institute of Hygiene of the Republic of Lithuania from the 60 unicipalities in the period from 2001 to 2019. Classification model selection procedure is illustrated on three data sets with class labels specified by the threshold to mortality index due to acute cardiovascular event, malignant neoplasms and diseases of the circulatory system. Presented critical comparison among proposed approach classifiers with various spatial autocorrelation indexes (decision threshold values) and classifier based hidden Markov model can aid in the selection of proper classification techniques for the spatio-temporal areal data.
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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.
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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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 phrastique et plus spécifiquement à celle des Constructions à Cas Multiples du coréen, il est soutenu que les marques dites casuelles du coréen ne sont pas de véritables marques casuelles, mais des têtes fonctionnelles, et que les Constructions à Cas Multiples du coréen s'obtiennent par la réitération de ces têtes fonctionnelles, suivie d'une opération d'Attraction.
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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 baseband signals. A dataset generated using the framework is considered, and is shown to be appropriately sized by having $11\%$ lower entropy than state-of-the-art datasets. Furthermore, unsupervised domain adaptation can allow for powerful generalized training via deep feature transforms on unlabeled evaluation-time signals. A novel Deep Reconstruction-Classification Network (DRCN) application is introduced, which attempts to maintain near-peak signal classification accuracy despite dataset bias, or perturbations on testing data unforeseen in training. Together, feature transforms and diverse training data generated from the proposed framework, teaching a range of plausible noise, can train a deep neural net to classify signals well in many real-world scenarios despite unforeseen perturbations.
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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 overall accuracy when compared to a standard benchmarking Boosting algorithm. The DataBoost-IM algorithm is developed to learn from two-class imbalanced data sets. In the DataBoost-IM approach, the class frequencies and the total weights against different classes within the ensemble's training set are rebalanced by adding new synthetic data. The DataBoost-IM method was evaluated, in terms of the F-measures, G-mean and overall accuracy, against seventeen highly and moderately imbalanced data sets using decision trees as base classifiers. (Abstract shortened by UMI.)
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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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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 coarse-grained modeling likely will require improved ribonucleotide-amino acid potentials as applied to coarse-grained structures. As a first step we downloaded 1,345 PDB files and clustered them with PISCES to obtain a non-redundant complex data set. The contacts were divided into nine types with DSSR according to the 3D structure of RNA and then 9 sets of potentials were calculated. The potentials were applied to score fifty thousand poses generated by FTDock for twenty-one standard RNA-protein complexes. The results compare favorably to existing RNA-protein potentials. Future research will optimize and test such combined potentials.

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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 as averaging, voting, weighted average, and optimised weighted method does not increase the accuracy of the combiner in comparison to the proposed advanced methods such as genetic programming and the coalition method. The different methods are studied in detail and analysed using different databases. The aim is to increase the accuracy of the combiner in comparison to the standard stand-alone classifiers. The proposed methods are based on generating a combiner formula using genetic programming, while the coalition is based on estimating the diversity of the classifiers such that a coalition is generated with better prediction accuracy. Standard accuracy measures are used, namely accuracy, sensitivity, specificity and area under the curve, in addition to training error accuracies such as the mean square error. The combiner methods are compared empirically with several stand-alone classifiers using neural network algorithms. Different types of neural network topologies are used to generate different models. Experimental results show that the combiner algorithms are superior in creating the most diverse and accurate classifier ensembles. Ensembles of the same models are generated to boost the accuracy of a single classifier type. An ensemble of 10 models of different initial weights is used to improve the accuracy. Experiments show a significant improvement over a single model classifier. Finally, two combining methods are studied, namely the genetic programming and coalition combination methods. The genetic programming algorithm is used to generate a formula for the classifiers’ combinations, while the coalition method is based on a simple algorithm that assigns linear combination weights based on the consensus theory. Experimental results of the same databases demonstrate the effectiveness of the proposed methods compared to conventional combining methods. The results show that the coalition method is better than genetic programming.
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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 strategies. Our framework combines three learning techniques: a) ensemble learning, b) artificial example generation, and c) diversity construction by reversely data re-labeling. As a meta-learner, DECIDL utilizes general supervised learning algorithms as base learners to build an ensemble committee. We create a standard benchmark data pool, which contains 30 highly skewed sets with diverse characteristics from different domains, in order to facilitate future research on imbalance data learning. We use this benchmark pool to evaluate and compare our DECIDL framework with several ensemble learning methods, namely under-bagging, over-bagging, SMOTE-bagging, and AdaBoost. Extensive experiments suggest that our DECIDL framework is comparable with other methods. The data sets, experiments and results provide a valuable knowledge base for future research on imbalance learning. We develop a simple but effective artificial example generation method for data balancing. Two new methods DBEG-ensemble and DECIDL-DBEG are then designed to improve the power of imbalance learning. Experiments show that these two methods are comparable to the state-of-the-art methods, e.g., GSVM-RU and SMOTE-bagging. Furthermore, we investigate learning on imbalanced data from a new angle—active learning. By combining active learning with the DECIDL framework, we show that the newly designed Active-DECIDL method is very effective for imbalance learning, suggesting the DECIDL framework is very robust and flexible.Lastly, we apply the proposed learning methods to a real-world bioinformatics problem—protein methylation prediction. Extensive computational results show that the DECIDL method does perform very well for the imbalanced data mining task. Importantly, the experimental results have confirmed our new contributions on this particular data learning problem.
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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-dimensional data. Since the GTM is non-linear, the relationship between data and its visual representation may be far from trivial, but a better understanding of this relationship can be gained by computing the so-called magnification factor. In essence, the magnification factor relates the distances between data points, as they appear when visualized, to the actual distances between those data points. There are two principal limitations of the basic GTM model. The computational effort required will grow exponentially with the intrinsic dimensionality of the density model. However, if the intended application is visualization, this will typically not be a problem. The other limitation is the inherent structure of the GTM, which makes it most suitable for modelling moderately curved probability distributions of approximately rectangular shape. When the target distribution is very different to that, theaim of maintaining an `interpretable' structure, suitable for visualizing data, may come in conflict with the aim of providing a good density model. The fact that the GTM is a probabilistic model means that results from probability theory and statistics can be used to address problems such as model complexity. Furthermore, this framework provides solid ground for extending the GTM to wider contexts than that of this thesis.
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Pyon, Yoon Soo. "Variant Detection Using Next Generation Sequencing Data." Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1347053645.

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Books on the topic "Generative classifiers"

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Grant, McDuling, ed. Instant leads: Everything you need to know about generating more business. 2nd ed. Brisbane, Qld: Action International, 2004.

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Silent heros of the Cold War declassified: The mysterious military plane crash on a Nevada mountain peak-- and the families who endured an abyss of silence for generation. Las Vegas, Nev: Stephens Press, 2009.

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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 efficacy, is not as meaningful as initially believed. Both classes have been demonstrated to safely improve psychotic symptoms in the acute phase of the illness and to reduce the risk of relapse in the maintenance phase of treatment. Because of the limited efficacy of antipsychotics in resolving the full range of schizophrenic psychopathology, adjunctive treatments are often used to reduce morbidity. This chapter reviews controlled trials of the pharmacological agents used to treat schizophrenia.
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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 strings, but a set of trees too: each one of the trees is associated with a string and illustrates the way this string is derived in the grammar.
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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 REM sleep. Lastly, the chapter reviews the putative role of microarousals and controversies regarding dopamine and acetylcholine in the generation of dreams.
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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 this avoid generating moral hazard? The accounts of exploitation analysed here are classified along two dimensions—historical vs. ahistorical and intentional vs. non-intentional—in their conceptions of unfairness, and the possibility of a hybrid account is explored.
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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 typology helps to show that values in science often operate not exclusively at the level of individual psychologies but also at the level of knowledge-generating social institutions.
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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 prolonged response, mediated by lymphocytes to clear infection and generate immunological memory. In this chapter, the functions of the innate and adaptive immune system are reviewed, together with the clinical features and investigation of acquired and inherited immune deficiencies.
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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 theory to be of presheaf type expressed in terms of the models of the theory in arbitrary Grothendieck toposes. This theorem is shown to admit a number of simpler corollaries which can be effectively applied in practice for testing whether a given theory is of presheaf type as well as for generating new examples of such theories.
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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, 67–83. Cham: 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, 374–87. Cham: 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, 479–90. Berlin, Heidelberg: 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, 453–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75256-1_41.

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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, 594–603. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01424-7_58.

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Tran, Quang Duy, and Fabio Di Troia. "Word Embeddings for Fake Malware Generation." In Silicon Valley Cybersecurity Conference, 22–37. Cham: 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 highly effective at generating image-like data that are similar to the actual data distribution. In this paper, we leverage the knowledge of generative modeling on opcode sequences and aim to generate malware samples by taking advantage of the contextualized embeddings from BERT. We obtained promising results when differentiating between real and generated samples. We observe that generated malware has such similar characteristics to actual malware that the classifiers are having difficulty in distinguishing between the two, in which the classifiers falsely identify the generated malware as actual malware almost $$90\%$$ of the time.
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Agarwal, Chirag, and Anh Nguyen. "Explaining Image Classifiers by Removing Input Features Using Generative Models." In Computer Vision – ACCV 2020, 101–18. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69544-6_7.

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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, 133–47. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-8746-5_10.

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Mery, Bruno, and Christian Retoré. "Classifiers, Sorts, and Base Types in the Montagovian Generative Lexicon and Related Type Theoretical Frameworks for Lexical Compositional Semantics." In Studies in Linguistics and Philosophy, 163–88. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50422-3_7.

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Zanda, Manuela, and Gavin Brown. "A Study of Semi-supervised Generative Ensembles." In Multiple Classifier Systems, 242–51. Berlin, Heidelberg: 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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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. New York, New York, USA: 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}. California: 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 conditional generative models, SDIMs scale on complex datasets, and can achieve comparable performance with discriminative counterparts. With SDIM, we could perform classification with rejection. Instead of always reporting a class label, SDIM only makes predictions when test samples' largest class conditional surpass some pre-chosen thresholds, otherwise they will be deemed as out of the data distributions, and be rejected. Our experiments show that SDIM with rejection policy can effectively reject illegal inputs, including adversarial examples and out-of-distribution samples.
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Yoshida, Hidefumi, Daichi Suzuo, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Takashi Machida, and Yoshiko Kojima. "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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Ding, Xiaoan, Tianyu Liu, Baobao Chang, Zhifang Sui, and Kevin Gimpel. "Discriminatively-Tuned Generative Classifiers for Robust Natural Language Inference." In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.emnlp-main.657.

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Mackowiak, Radek, Lynton Ardizzone, Ullrich Kothe, and Carsten Rother. "Generative Classifiers as a Basis for Trustworthy Image Classification." In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021. http://dx.doi.org/10.1109/cvpr46437.2021.00299.

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Zhu, Yao, Jiacheng Ma, Jiacheng Sun, Zewei Chen, Rongxin Jiang, Yaowu Chen, and Zhenguo Li. "Towards Understanding the Generative Capability of Adversarially Robust Classifiers." In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.00763.

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Sun, Xin, Xin An, Shuo Xu, Liyuan Hao, and Jinghong Li. "Identifying Important Citations by Incorporating Generative Model into Discriminative Classifiers." In IMMS 2020: 2020 3rd International Conference on Information Management and Management Science. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3416028.3416043.

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Tiwari, Lokender, Anish Madan, Saket Anand, and Subhashis Banerjee. "REGroup: Rank-aggregating Ensemble of Generative Classifiers for Robust Predictions." In 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2022. http://dx.doi.org/10.1109/wacv51458.2022.00388.

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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. Fort Belvoir, VA: Defense Technical Information Center, June 1994. http://dx.doi.org/10.21236/ada286028.

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Dzanku, Fred M., and Louis S. Hodey. Achieving Inclusive Oil Palm Commercialisation in Ghana. Institute of Development Studies (IDS), February 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 achieved its potential, and this begs the question, why? Although it is known in general that commercialisation potential and its benefits are not equally distributed across groups, it is not clear how and why different subgroups (women, men, youth) might benefit differently from the oil palm economy. This brief addresses why different groups of smallholders (women, men, youth) benefit unequally from oil palm value chains, and how returns to oil palm production and marketing could become more inclusive.
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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, March 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 or fee in the previous year, representing on average 9.5 per cent of annual income. We also find that, among households that contribute, poorer ones contribute larger amounts than richer ones, with higher incidence in relation to their income. Further, in line with theory and expectations, informal payments have inequitable community-level effects, with individuals in wealthier communities making more informal payments than in poorer ones and, correspondingly, having access to a greater number of public goods. We then consider four explanations for the prevalence of IRG. First, IRG clearly fills gaps left by weak state capacity. Relatedly, we show that IRG can bolster perceptions and legitimacy of the state, indicating that sub-national governments may actually benefit from informal taxation. Second, informal taxing authorities are more effective tax collectors than the state, with informal taxing authorities having greater legitimacy and taxpayers perceiving informal payments to be fairer than those levied by the state. Third, dispelling the possibility that informal payments should be classified as user fees, taxpayers overwhelmingly expect nothing in return for their contributions. Fourth, in contrast to hypotheses that informal payments may be voluntary, taxpayers associate informal payments with punishment and informal institutions of enforcement. Our research reinforces the importance of IRG to public goods provision in weak formal institutional contexts, to everyday citizens, and to policymakers attempting to extend the influence of the federal state in south-central Somalia. Foremost, informal tax institutions need to be incorporated within analyses of taxation, service delivery, social protection, and equity. At the same time, our findings of the complementary nature of IRG and district-level governance and of the relative efficiency of revenue generation by local leaders have important implications for understanding statebuilding processes from below. Indeed, our findings suggest that governments may have little incentive to extend their taxing authority in some fragile contexts.
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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, February 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 tolerance, and growth enhancement to host plants, 3- compare fslm1 sequences and their genomic locations in the different species, and 4- document the colonization process of each Colletotrichum species.It was demonstrated that wildtype pathogenic Colletotrichum isolates, can be converted by mutation from expressing a pathogenic lifestyle to symbionts expressing non-pathogenic lifestyles. In the US, mutants of Colletotrichum were isolated by homologous gene disruption using a vector containing a disrupted FSlm1 sequence while in Israel, C. acutatum mutants were selected by restriction enzyme mediated integration (REMI) transformation. One group (US) of non-pathogenic mutants conferred disease protection against pathogenic species of Colletotrichum, Fusarium, and Phytophthora; drought tolerance; and growth enhancement to host plants. These mutants were defined as mutualists and disease resistance correlated to a decrease in the time required for hosts to activate defense systems when exposed to virulent fungi. The second group (Israel) of non-pathogenic mutants did not confer disease resistance and were classified as commensals. In addition, we demonstrated that wildtype pathogenic Colletotrichum species can express non-pathogenic lifestyles, including mutualism, on plants they colonize asymptomatically. The expected long term contribution of this research to agriculture in the US and Israel is threefold. Host-specific mutualists will be utilized in the various crops to confer (1) disease resistance to reduce dependence on chemical fungicides; (2) drought tolerance to reduce water consumption for irrigation; (3) growth enhancement to increase yields.
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