Relevant bibliographies by topics / Linear classifier

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

Academic literature on the topic 'Linear classifier'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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Journal articles on the topic "Linear classifier"

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Chen, Songcan, and Xubing Yang. "Alternative linear discriminant classifier." Pattern Recognition 37, no. 7 (July 2004): 1545–47. http://dx.doi.org/10.1016/j.patcog.2003.11.008.

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Barlach, Flemming. "A linear classifier design approach." Pattern Recognition 24, no. 9 (January 1991): 871–77. http://dx.doi.org/10.1016/0031-3203(91)90006-q.

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Gyamfi, Kojo Sarfo, James Brusey, Andrew Hunt, and Elena Gaura. "Linear classifier design under heteroscedasticity in Linear Discriminant Analysis." Expert Systems with Applications 79 (August 2017): 44–52. http://dx.doi.org/10.1016/j.eswa.2017.02.039.

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Ellis, Steven P. "When a Constant Classifier is as Good as Any Linear Classifier." Communications in Statistics - Theory and Methods 40, no. 21 (November 2011): 3800–3811. http://dx.doi.org/10.1080/03610926.2010.498650.

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Pascadi, Manuela A., and Mihai V. Pascadi. "Non‐linear Trainable Classifier in IRd." Kybernetes 22, no. 1 (January 1993): 13–21. http://dx.doi.org/10.1108/eb005953.

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Zhu, Changming, Xiang Ji, Chao Chen, Rigui Zhou, Lai Wei, and Xiafen Zhang. "Improved linear classifier model with Nyström." PLOS ONE 13, no. 11 (November 5, 2018): e0206798. http://dx.doi.org/10.1371/journal.pone.0206798.

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Li Yujian, Liu Bo, Yang Xinwu, Fu Yaozong, and Li Houjun. "Multiconlitron: A General Piecewise Linear Classifier." IEEE Transactions on Neural Networks 22, no. 2 (February 2011): 276–89. http://dx.doi.org/10.1109/tnn.2010.2094624.

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Bertò, Giulia, Daniel Bullock, Pietro Astolfi, Soichi Hayashi, Luca Zigiotto, Luciano Annicchiarico, Francesco Corsini, et al. "Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation." NeuroImage 224 (January 2021): 117402. http://dx.doi.org/10.1016/j.neuroimage.2020.117402.

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Kundu, Anirban, Guanxiong Xu, and Chunlin Ji. "Structural Analysis of Cloud Classifier." International Journal of Cloud Applications and Computing 4, no. 1 (January 2014): 63–75. http://dx.doi.org/10.4018/ijcac.2014010106.

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In this paper, structural analysis of Cloud classifier is going to be discussed to make a clear distinction between linear and non-linear Cloud structures. Cloud manager is responsible for managing different activities within Cloud using distinct fields. Cloud path protocol has been defined to disseminate information from one node to another in a efficient way. Broadcasting complexities of linear and non-linear Cloud are also being projected in this paper. Mathematical expressions have been established for defining different performance factors of the Cloud network. In practical scenario, non-linear Cloud structure is more relevant than a linear Cloud structure.
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HUANG, KAI-YI, and P. W. MAUSEL. "Spatial post-processing of spectrally classified video images by a piecewise linear classifier." International Journal of Remote Sensing 14, no. 13 (September 1993): 2563–74. http://dx.doi.org/10.1080/01431169308904293.

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Dissertations / Theses on the topic "Linear classifier"

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Medonza, Dharshan C. "AUTOMATIC DETECTION OF SLEEP AND WAKE STATES IN MICE USING PIEZOELECTRIC SENSORS." UKnowledge, 2006. http://uknowledge.uky.edu/gradschool_theses/271.

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Currently technologies such as EEG, EMG and EOG recordings are the established methods used in the analysis of sleep. But if these methods are to be employed to study sleep in rodents, extensive surgery and recovery is involved which can be both time consuming and costly. This thesis presents and analyzes a cost effective, non-invasive, high throughput system for detecting the sleep and wake patterns in mice using a piezoelectric sensor. This sensor was placed at the bottom of the mice cages to monitor the movements of the mice. The thesis work included the development of the instrumentation and signal acquisition system for recording the signals critical to sleep and wake classification. Classification of the mouse sleep and wake states were studied for a linear classifier and a Neural Network classifier based on 23 features extracted from the Power Spectrum (PS), Generalized Spectrum (GS), and Autocorrelation (AC) functions of short data intervals. The testing of the classifiers was done on two data sets collected from two mice, with each data set having around 5 hours of data. A scoring of the sleep and wake states was also done via human observation to aid in the training of the classifiers. The performances of these two classifiers were analyzed by looking at the misclassification error of a set of test features when run through a classifier trained by a set of training features. The best performing features were selected by first testing each of the 23 features individually in a linear classifier and ranking them according to their misclassification rate. A test was then done on the 10 best individually performing features where they were grouped in all possible combinations of 5 features to determine the feature combinations leading to the lowest error rates in a multi feature classifier. From this test 5 features were eventually chosen to do the classification. It was found that the features related to the signal energy and the spectral peaks in the 3Hz range gave the lowest errors. Error rates as low as 4% and 9% were achieved from a 5-feature linear classifier for the two data sets. The error rates from a 5-feature Neural Network classifier were found to be 6% and 12% respectively for these two data sets.
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Georgatzis, Konstantinos. "Dynamical probabilistic graphical models applied to physiological condition monitoring." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28838.

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Intensive Care Units (ICUs) host patients in critical condition who are being monitored by sensors which measure their vital signs. These vital signs carry information about a patient’s physiology and can have a very rich structure at fine resolution levels. The task of analysing these biosignals for the purposes of monitoring a patient’s physiology is referred to as physiological condition monitoring. Physiological condition monitoring of patients in ICUs is of critical importance as their health is subject to a number of events of interest. For the purposes of this thesis, the overall task of physiological condition monitoring is decomposed into the sub-tasks of modelling a patient’s physiology a) under the effect of physiological or artifactual events and b) under the effect of drug administration. The first sub-task is concerned with modelling artifact (such as the taking of blood samples, suction events etc.), and physiological episodes (such as bradycardia), while the second sub-task is focussed on modelling the effect of drug administration on a patient’s physiology. The first contribution of this thesis is the formulation, development and validation of the Discriminative Switching Linear Dynamical System (DSLDS) for the first sub-task. The DSLDS is a discriminative model which identifies the state-of-health of a patient given their observed vital signs using a discriminative probabilistic classifier, and then infers their underlying physiological values conditioned on this status. It is demonstrated on two real-world datasets that the DSLDS is able to outperform an alternative, generative approach in most cases of interest, and that an a-mixture of the two models achieves higher performance than either of the two models separately. The second contribution of this thesis is the formulation, development and validation of the Input-Output Non-Linear Dynamical System (IO-NLDS) for the second sub-task. The IO-NLDS is a non-linear dynamical system for modelling the effect of drug infusions on the vital signs of patients. More specifically, in this thesis the focus is on modelling the effect of the widely used anaesthetic drug Propofol on a patient’s monitored depth of anaesthesia and haemodynamics. A comparison of the IO-NLDS with a model derived from the Pharmacokinetics/Pharmacodynamics (PK/PD) literature on a real-world dataset shows that significant improvements in predictive performance can be provided without requiring the incorporation of expert physiological knowledge.
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Ozer, Gizem. "Fuzzy Classification Models Based On Tanaka." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610785/index.pdf.

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In some classification problems where human judgments, qualitative and imprecise data exist, uncertainty comes from fuzziness rather than randomness. Limited number of fuzzy classification approaches is available for use for these classification problems to capture the effect of fuzzy uncertainty imbedded in data. The scope of this study mainly comprises two parts: new fuzzy classification approaches based on Tanaka&rsquo
s Fuzzy Linear Regression (FLR) approach, and an improvement of an existing one, Improved Fuzzy Classifier Functions (IFCF). Tanaka&rsquo
s FLR approach is a well known fuzzy regression technique used for the prediction problems including fuzzy type of uncertainty. In the first part of the study, three alternative approaches are presented, which utilize the FLR approach for a particular customer satisfaction classification problem. A comparison of their performances and their applicability in other cases are discussed. In the second part of the study, the improved IFCF method, Nonparametric Improved Fuzzy Classifier Functions (NIFCF), is presented, which proposes to use a nonparametric method, Multivariate Adaptive Regression Splines (MARS), in clustering phase of the IFCF method. NIFCF method is applied on three data sets, and compared with Fuzzy Classifier Function (FCF) and Logistic Regression (LR) methods.
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Fonseca, Everthon Silva. "Wavelets, predição linear e LS-SVM aplicados na análise e classificação de sinais de vozes patológicas." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-04072008-094655/.

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Neste trabalho, foram utilizadas as vantagens da ferramenta matemática de análise temporal e espectral, a transformada wavelet discreta (DWT), além dos coeficientes de predição linear (LPC) e do algoritmo de inteligência artificial, Least Squares Support Vector Machines (LS-SVM), para aplicações em análise de sinais de voz e classificação de vozes patológicas. Inúmeros trabalhos na literatura têm demonstrado o grande interesse existente por ferramentas auxiliares ao diagnóstico de patologias da laringe. Os componentes da DWT forneceram parâmetros de medida para a análise e classificação das vozes patológicas, principalmente aquelas provenientes de pacientes com edema de Reinke e nódulo nas pregas vocais. O banco de dados com as vozes patológicas foi obtido do Departamento de Otorrinolaringologia e Cirurgia de Cabeça e Pescoço do Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto (FMRP-USP). Utilizando-se o algoritmo de reconhecimento de padrões, LS-SVM, mostrou-se que a combinação dos componentes da DWT de Daubechies com o filtro LP inverso levou a um classificador de bom desempenho alcançando mais de 90% de acerto na classificação das vozes patológicas.
The main objective of this work was to use the advantages of the time-frequency analysis mathematical tool, discrete wavelet transform (DWT), besides the linear prediction coefficients (LPC) and the artificial intelligence algorithm, Least Squares Support Vector Machines (LS-SVM), for applications in voice signal analysis and classification of pathological voices. A large number of works in the literature has been shown that there is a great interest for auxiliary tools to the diagnosis of laryngeal pathologies. DWT components gave measure parameters for the analysis and classification of pathological voices, mainly that ones from patients with Reinke\'s edema and nodule in the vocal folds. It was used a data bank with pathological voices from the Otolaryngology and the Head and Neck Surgery sector of the Clinical Hospital of the Faculty of Medicine at Ribeirão Preto, University of Sao Paulo (FMRP-USP), Brazil. Using the automatic learning algorithm applied in pattern recognition problems, LS-SVM, results have showed that the combination of Daubechies\' DWT components and inverse LP filter leads to a classifier with good performance reaching more than 90% of accuracy in the classification of the pathological voices.
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Zhang, Angang. "Some Advances in Classifying and Modeling Complex Data." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/77958.

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In statistical methodology of analyzing data, two of the most commonly used techniques are classification and regression modeling. As scientific technology progresses rapidly, complex data often occurs and requires novel classification and regression modeling methodologies according to the data structure. In this dissertation, I mainly focus on developing a few approaches for analyzing the data with complex structures. Classification problems commonly occur in many areas such as biomedical, marketing, sociology and image recognition. Among various classification methods, linear classifiers have been widely used because of computational advantages, ease of implementation and interpretation compared with non-linear classifiers. Specifically, linear discriminant analysis (LDA) is one of the most important methods in the family of linear classifiers. For high dimensional data with number of variables p larger than the number of observations n occurs more frequently, it calls for advanced classification techniques. In Chapter 2, I proposed a novel sparse LDA method which generalizes LDA through a regularized approach for the two-class classification problem. The proposed method can obtain an accurate classification accuracy with attractive computation, which is suitable for high dimensional data with p>n. In Chapter 3, I deal with the classification when the data complexity lies in the non-random missing responses in the training data set. Appropriate classification method needs to be developed accordingly. Specifically, I considered the "reject inference problem'' for the application of fraud detection for online business. For online business, to prevent fraud transactions, suspicious transactions are rejected with unknown fraud status, yielding a training data with selective missing response. A two-stage modeling approach using logistic regression is proposed to enhance the efficiency and accuracy of fraud detection. Besides the classification problem, data from designed experiments in scientific areas often have complex structures. Many experiments are conducted with multiple variance sources. To increase the accuracy of the statistical modeling, the model need to be able to accommodate more than one error terms. In Chapter 4, I propose a variance component mixed model for a nano material experiment data to address the between group, within group and within subject variance components into a single model. To adjust possible systematic error introduced during the experiment, adjustment terms can be added. Specifically a group adaptive forward and backward selection (GFoBa) procedure is designed to select the significant adjustment terms.
Ph. D.
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Gul, Ahmet Bahtiyar. "Holistic Face Recognition By Dimension Reduction." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1056738/index.pdf.

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Face recognition is a popular research area where there are different approaches studied in the literature. In this thesis, a holistic Principal Component Analysis (PCA) based method, namely Eigenface method is studied in detail and three of the methods based on the Eigenface method are compared. These are the Bayesian PCA where Bayesian classifier is applied after dimension reduction with PCA, the Subspace Linear Discriminant Analysis (LDA) where LDA is applied after PCA and Eigenface where Nearest Mean Classifier applied after PCA. All the three methods are implemented on the Olivetti Research Laboratory (ORL) face database, the Face Recognition Technology (FERET) database and the CNN-TURK Speakers face database. The results are compared with respect to the effects of changes in illumination, pose and aging. Simulation results show that Subspace LDA and Bayesian PCA perform slightly well with respect to PCA under changes in pose
however, even Subspace LDA and Bayesian PCA do not perform well under changes in illumination and aging although they perform better than PCA.
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Tuma, Carlos Cesar Mansur. "Aprendizado de máquina baseado em separabilidade linear em sistema de classificação híbrido-nebuloso aplicado a problemas multiclasse." Universidade Federal de São Carlos, 2009. https://repositorio.ufscar.br/handle/ufscar/407.

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Made available in DSpace on 2016-06-02T19:05:36Z (GMT). No. of bitstreams: 1 2598.pdf: 3349204 bytes, checksum: 01649491fd1f03aa5a11b9191727f88b (MD5) Previous issue date: 2009-06-29
Financiadora de Estudos e Projetos
This master thesis describes an intelligent classifier system applied to multiclass non-linearly separable problems called Slicer. The system adopts a low computacional cost supervised learning strategy (evaluated as ) based on linear separability. During the learning period the system determines a set of hyperplanes associated to oneclass regions (sub-spaces). In classification tasks the classifier system uses the hyperplanes as a set of if-then-else rules to infer the class of the input attribute vector (non classified object). Among other characteristics, the intelligent classifier system is able to: deal with missing attribute values examples; reject noise examples during learning; adjust hyperplane parameters to improve the definition of the one-class regions; and eliminate redundant rules. The fuzzy theory is considered to design a hybrid version with features such as approximate reasoning and parallel inference computation. Different classification methods and benchmarks are considered for evaluation. The classifier system Slicer reaches acceptable results in terms of accuracy, justifying future investigation effort.
Este trabalho de mestrado descreve um sistema classificador inteligente aplicado a problemas multiclasse não-linearmente separáveis chamado Slicer. O sistema adota uma estratégia de aprendizado supervisionado de baixo custo computacional (avaliado em ) baseado em separabilidade linear. Durante o período de aprendizagem o sistema determina um conjunto de hiperplanos associados a regiões de classe única (subespaços). Nas tarefas de classificação o sistema classificador usa os hiperplanos como um conjunto de regras se-entao-senao para inferir a classe do vetor de atributos dado como entrada (objeto a ser classificado). Entre outras caracteristicas, o sistema classificador é capaz de: tratar atributos faltantes; eliminar ruídos durante o aprendizado; ajustar os parâmetros dos hiperplanos para obter melhores regiões de classe única; e eliminar regras redundantes. A teoria nebulosa é considerada para desenvolver uma versão híbrida com características como raciocínio aproximado e simultaneidade no mecanismo de inferência. Diferentes métodos de classificação e domínios são considerados para avaliação. O sistema classificador Slicer alcança resultados aceitáveis em termos de acurácia, justificando investir em futuras investigações.
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Šenkýř, Ivo. "Detekce objektů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217232.

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This diploma thesis deals with a problem of spores venturia inaequlis recognition. These spores are captured on a special tape which is then analyzed using a microscope. The tape can be analyzed by a laboratorian or by the program Sporedetect v3. This program provides functions for complete picture processing and object recognition. In this diploma thesis, there are also described ways to automatically control a sliding stage of a microscope utilizing motorized translation stages and linear actuators. The information about automatic control of a microscope stage was obtained from catalogues of the companies Standa and Edmundoptics.
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Bust, Reg. "Orthogonal models for cross-classified observations." Doctoral thesis, University of Cape Town, 1987. http://hdl.handle.net/11427/15852.

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Includes bibliography.
This thesis describes methods of constructing models for cross-classified categorical data. In particular we discuss the construction of a class of approximating models and the selection of the most suitable model in the class. Examples of application are used to illustrate the methodology. The main purpose of the thesis is to demonstrate that it is both possible and advantageous to construct models which are specifically designed for the particular application under investigation. We believe that the methods described here allow the statistician to make good use of any expert knowledge which the client (typically a non-statistician) might possess on the subject to which the data relate.
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Černá, Tereza. "Detekce a rozpoznání registrační značky vozidla pro analýzu dopravy." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234966.

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This thesis describes the design and development of a system for detection and recognition of license plates. The work is divided into three basic parts: licence plates detection, finding of character positions and optical character recognition. To fullfill the goal of this work, a new dataset was taken. It contains 2814 license plates used for training classifiers and 2620 plates to evaluate the success rate of the system. Cascade Classifier was used to train detector of licence plates, which has success rate up to 97.8 %. After that, pozitions of individual characters were searched in detected pozitions of licence plates. If there was no character found, detected pozition was not the licence plate. Success rate of licence plates detection with all the characters found is up to 88.5 %. Character recognition is performed by SVM classifier. The system detects successfully with no errors up to 97.7 % of all licence plates.
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Books on the topic "Linear classifier"

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Cristante, Francesca. Item analysis: An approach using log-linear models for the study of cross-classified tables. Bologna: Pàtron editore, 1987.

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Fienberg, Stephen E. The analysis of cross-classified categorical data. 2nd ed. Cambridge, Mass: MIT Press, 1989.

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author, Sarich Marco 1985, ed. Metastability and Markov state models in molecular dynamics: Modeling, analysis, algorithmic approaches. Providence, Rhode Island: American Mathematical Society, 2013.

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

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This article reviews the literature concerning supervised and unsupervised classification of functional data. It first explains the meaning of unsupervised classification vs. supervised classification before discussing the supervised classification problem in the infinite-dimensional case, showing that its formal statement generally coincides with that of discriminant analysis in the classical multivariate case. It then considers the optimal classifier and plug-in rules, empirical risk and empirical minimization rules, linear discrimination rules, the k nearest neighbor (k-NN) method, and kernel rules. It also describes classification based on partial least squares, classification based on reproducing kernels, and depth-based classification. Finally, it examines unsupervised classification methods, focusing on K-means for functional data, K-means for data in a Hilbert space, and impartial trimmed K-means for functional data. Some practical issues, in particular real-data examples and simulations, are reviewed and some selected proofs are given.
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Fienberg, Stephen. The Analysis of Cross-Classified Categorical Data. 2nd ed. Springer, 2007.

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Caramello, Olivia. Examples of theories of presheaf type. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198758914.003.0011.

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This chapter discusses several classical as well as new examples of theories of presheaf type from the perspective of the theory developed in the previous chapters. The known examples of theories of presheaf type that are revisited in the course of the chapter include the theory of intervals (classified by the topos of simplicial sets), the theory of linear orders, the theory of Diers fields, the theory of abstract circles (classified by the topos of cyclic sets) and the geometric theory of finite sets. The new examples include the theory of algebraic (or separable) extensions of a given field, the theory of locally finite groups, the theory of vector spaces with linear independence predicates and the theory of lattice-ordered abelian groups with strong unit.
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Ausink, John A. An Optimization Approach to Workforce Planning for the Information Technology Field. RAND Corporation, 2002.

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Pittenger, Christopher. The Pharmacological Treatment of Refractory OCD. Edited by Christopher Pittenger. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228163.003.0041.

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Evidence-based interventions for OCD, both psychotherapeutic and pharmacological, are of benefit to many. However, even when optimally deployed, first- and second-line treatments leave a substantial fraction of patients—perhaps as many as 20% to 25%—without meaningful improvement. Furthermore, many who are classified as “responders” to first- and second-line treatments continue to have substantial residual symptoms and attendant morbidity. This chapter reviews various pharmacological strategies that have been used for the treatment of refractory OCD, including agents targeting serotonin, dopamine, and glutamate neurotransmission. Although the evidence base supporting the use of these agents is not as robust as it is for first-line interventions, many have shown promise in some studies. The prevalence of refractory OCD symptoms means that such pharmacological strategies must frequently be considered in clinical practice, despite the lack of definitive guidance from controlled studies.
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Schneider, Edgar W. Models of English in the World. Edited by Markku Filppula, Juhani Klemola, and Devyani Sharma. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199777716.013.001.

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This chapter systematically surveys conceptual frameworks (models) that have been suggested to identify similarities between World Englishes and to classify them accordingly. The earliest suggestions along these lines were static models, which either worked out historically based relationships between national varieties, having branched off in a family-tree-like manner, or classified countries based on whether English is used as a native, second or foreign language in them. Other early categorizations emphasized the global, national or regional outreach of varieties (in “hub-and-spoke” models) or variety types based on sociolinguistic settings in communities and their resulting linguistic properties. In contrast, recent models emphasize the evolutionary or even cyclic character of varieties; these include Trudgill’s deterministic theory and, very widely accepted nowadays, Schneider’s Dynamic Model, which is broadly outlined, including a brief discussion of some applications of and reactions to it.
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Camper, Martin. Ambiguity. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190677121.003.0002.

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Chapter 2 examines disputes over ambiguity, in which interpreters argue over a single linguistic form that evokes distinct alternative meanings. The chapter classifies three types of ambiguity according to contemporary linguistic theory, details common lines of argument for supporting interpretations of ambiguities, and explains the differences between interpreting an ambiguity as unintentional versus intentional. The chapter offers an extended rhetorical analysis of the controversy surrounding Phillis Wheatley’s 1768 poem “On Being Brought from Africa to America,” which has been criticized as an expression of racial self-hatred. Literary critics in defense of Wheatley have argued the poem contains intentional ambiguities that covertly express Wheatley’s anti-racist and slavery views. This case illustrates that arguers can claim a text contains a coded message by uncovering additional meanings through its ambiguities. The various examples in the chapter highlight the important role ambiguity plays in shifting our interpretations of texts and their authors.
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Book chapters on the topic "Linear classifier"

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Gopi, E. S. "Linear Classifier Techniques." In Pattern Recognition and Computational Intelligence Techniques Using Matlab, 31–67. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22273-4_2.

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Skurichina, Marina, and Robert P. W. Duin. "Boosting in Linear Discriminant Analysis." In Multiple Classifier Systems, 190–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45014-9_18.

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Hand, David J., Niall M. Adams, and Mark G. Kelly. "Multiple Classifier Systems Based on Interpretable Linear Classifiers." In Multiple Classifier Systems, 136–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-48219-9_14.

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Maudes, Jesús, Juan J. Rodríguez, and César García-Osorio. "Disturbing Neighbors Ensembles for Linear SVM." In Multiple Classifier Systems, 191–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02326-2_20.

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Reid, Sam, and Greg Grudic. "Regularized Linear Models in Stacked Generalization." In Multiple Classifier Systems, 112–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02326-2_12.

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Gama, João. "A Linear-Bayes Classifier." In Advances in Artificial Intelligence, 269–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44399-1_28.

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Ahmad, Amir, and Gavin Brown. "A Study of Random Linear Oracle Ensembles." In Multiple Classifier Systems, 488–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02326-2_49.

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Pękalska, Elżbieta, Marina Skurichina, and Robert P. W. Duin. "Combining Fisher Linear Discriminants for Dissimilarity Representations." In Multiple Classifier Systems, 117–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45014-9_11.

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Janeliūnas, Arūnas, and Šarūnas Raudys. "Reduction of the Boasting Bias of Linear Experts." In Multiple Classifier Systems, 242–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45428-4_24.

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Fumera, Giorgio, and Fabio Roli. "Linear Combiners for Classifier Fusion: Some Theoretical and Experimental Results." In Multiple Classifier Systems, 74–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44938-8_8.

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Conference papers on the topic "Linear classifier"

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Fan, Xiannian, and Ke Tang. "Enhanced Maximum AUC Linear Classifier." In 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2010. http://dx.doi.org/10.1109/fskd.2010.5569339.

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Wang, Hai, and Fei Hao. "An efficient linear regression classifier." In 2012 IEEE International Conference on Signal Processing, Computing and Control (ISPCC). IEEE, 2012. http://dx.doi.org/10.1109/ispcc.2012.6224355.

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Festila, L., R. Groza, M. Cirlugea, and A. Fazakas. "Log-Domain Linear SVM Classifier." In 2007 14th International Conference on Mixed Design of Integrated Circuits and Systems. IEEE, 2007. http://dx.doi.org/10.1109/mixdes.2007.4286172.

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Abdurrab, Abdul A., Michael T. Manry, Jiang Li, Sanjeev S. Malalur, and Robert G. Gore. "A Piecewise Linear Network Classifier." In 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4371222.

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Zhang, Fei, Wei Jie Huang, and Patrick P. K. Chan. "Hardness of evasion of multiple classifier system with non-linear classifiers." In 2014 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2014. http://dx.doi.org/10.1109/icwapr.2014.6961290.

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Mladenić, Dunja, Janez Brank, Marko Grobelnik, and Natasa Milic-Frayling. "Feature selection using linear classifier weights." In the 27th annual international conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1008992.1009034.

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Hartono, Pitoyo, and Shuji Hashimoto. "Ensemble as a Piecewise Linear Classifier." In 2006 Sixth International Conference on Hybrid Intelligent Systems. IEEE, 2006. http://dx.doi.org/10.1109/his.2006.264915.

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Wu, Jianxin, Matthew D. Mullin, and James M. Rehg. "Linear Asymmetric Classifier for cascade detectors." In the 22nd international conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1102351.1102476.

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Pakri, Noor Azilah, Abdul Razak Hussain, and Khairul Azhar Kasmiran. "Linear machine weight adaptation in a genetic programming classifier that classifies medical data." In 2008 International Conference on Computer and Communication Engineering (ICCCE). IEEE, 2008. http://dx.doi.org/10.1109/iccce.2008.4580603.

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Feng, Qingxiang, Qi Zhu, Chun Yuan, and Ivan Lee. "Multi-linear regression coefficient classifier for recognition." In 2016 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2016. http://dx.doi.org/10.1109/cec.2016.7743950.

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Reports on the topic "Linear classifier"

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Chavez, Wesley. An Exploration of Linear Classifiers for Unsupervised Spiking Neural Networks with Event-Driven Data. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6323.

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