Relevant bibliographies by topics / AMS Classification

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

Academic literature on the topic 'AMS Classification'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "AMS Classification"

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Benyacoub, Badreddine, Souad ElBernoussi, Abdelhak Zoglat, and EL Moudden Ismail. "Classification with hidden Markov model." Applied Mathematical Sciences 8 (2014): 2483–96. http://dx.doi.org/10.12988/ams.2014.42129.

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Afifi, Larbi, Meriem Joundi, Najia Amimi, and Mohamed Bahadi. "Asymptotic classification of distributed linear systems." Applied Mathematical Sciences 7 (2013): 2537–54. http://dx.doi.org/10.12988/ams.2013.13229.

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Koceić-Bilan, Nikola, and Ivančica Mirošević. "On classification of morphisms by box-homotopy." Acta mathematica Spalatensia 1, no. 1 (2021): 97–103. http://dx.doi.org/10.32817/ams.1.1.8.

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In [1] the authors proposed a generalization of the notion of homotopy, a relation called to be box-homotopic, proven to be an equivalence relation on Top(X,Y) and well-adjusted with the composition. In this article we prove that all the mappings of Top(X,Y) are box-homotopic, that is, the classification of morphisms by the box-homotopy relation is the coarsest.
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Ravikumar, S., and A. Shanmugam. "WBC image segmentation and classification using RVM." Applied Mathematical Sciences 8 (2014): 2227–37. http://dx.doi.org/10.12988/ams.2014.43191.

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Arafi, Ayoub, Youssef Safi, Rkia Fajr, and Abdelaziz Bouroumi. "Classification of mammographic images using artificial neural networks." Applied Mathematical Sciences 7 (2013): 4415–23. http://dx.doi.org/10.12988/ams.2013.35293.

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Yukhymenko, Mykola, Ruslan Ostroha, and Artem Evtuhov. "Pneumatic Classification of Granular Materials in Rhomb-Shaped Apparatus." Acta Mechanica Slovaca 25, no. 3 (2021): 46–51. http://dx.doi.org/10.21496/ams.2021.032.

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Beltrami, Monica, and Arinei Carlos Lindbeck da Silva. "Grid-quadtree algorithm for support vector classification parameters selection." Applied Mathematical Sciences 9 (2015): 75–82. http://dx.doi.org/10.12988/ams.2015.411938.

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Tsitsiashvili, G. Sh, V. P. Bulgakov, and A. S. Losev. "Hierarchical classification of directed graph with cyclically equivalent nodes." Applied Mathematical Sciences 10 (2016): 2529–36. http://dx.doi.org/10.12988/ams.2016.66192.

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Niyonsaba, Therence, Guy Darcy Remesha, Mwetaminwa Muyengo Blaise, Dionis Nizigiyimana, and Jean de Dieu Nkurunziza. "Statistical classification of the state of newborns in Burundi." Applied Mathematical Sciences 19, no. 1 (2025): 11–20. https://doi.org/10.12988/ams.2025.919190.

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This work aims to identify the various health districts in Burundi requiring more than others specific support for improving child health while applying the exploratory method. The latter consists in calculating, for the districts surveyed as part of the development of the statistical yearbook, indicators assessing the state of health of the newborns. For this end, a statistical classification was performed. These statistical analyzes use a set of data totaling 14 variables relating to the state of health of newborns.
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Delhom, Christopher D., James Knowlton, Vikki B. Martin, and Cody Blake. "COTTON GINNERS HANDBOOK: The Classification of Cotton." Journal of Cotton Science 24, no. 4 (2020): 189–96. http://dx.doi.org/10.56454/eeeg9440.

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Cotton classification is the process of using official standards and standardized procedures to measure the physical attributes of raw cotton. The USDA Agricultural Marketing Service (AMS) classes essentially all cotton grown in the U.S. Samples are collected from each bale at the gin or warehouse for classing at one of 10 AMS classing offices. Automated instrumentation is used to assess fiber length, length uniformity, strength, color, micronaire, and non-lint content. Automation has allowed AMS to continue classing cotton efficiently as crop size has expanded. A human classer examines every classing sample to determine if there is any extraneous matter present that would alter the value of the bale. Classing results are matched with a permanent bale identification number that is assigned to each bale at the time of sampling. This identification number allows the bale to be identified throughout the supply chain, from packaging of the bale at the gin through consumption on the textile mill floor.
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Dissertations / Theses on the topic "AMS Classification"

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Markert, Erik, Hailu Wang, Göran Herrmann, and Ulrich Heinkel. "Kostenmodellierung mit SystemC/System-AMS." Universitätsbibliothek Chemnitz, 2007. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200700902.

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In diesem Beitrag wird eine Methode zur Beschreibung von Kostenfaktoren und deren Verknüpfung über Hierarchiegrenzen hinweg dargestellt. Sie eignet sich sowohl für rein digitale Systeme mit Softwareanteilen als auch für gemischt analog/digitale Systeme. Damit ist sie im Hardware-Software Codesign und im Analog-Digital Codesign zum Vergleich verschiedener Systemkompositionen anwendbar. Die Implementierung mit C++ ermöglicht neben einer Nutzung mit digitalem SystemC auch den Einsatz mit der analogen SystemC-Erweiterung SystemC-AMS und vereinfacht die Nutzung gegenüber einer vorhandenen VHDL-Implementierung. Als Anwendungsbeispiel fungieren Komponenten eines Systems zur Inertialnavigation.
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Schlegel, Michael. "Mixed-Level-Simulation heterogener Systeme mit VHDL-AMS durch Multi-Architecture-Modellierung." Doctoral thesis, Universitätsbibliothek Chemnitz, 2005. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200501903.

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Die Simulation heterogener Systeme auf hoher Abstraktionsebene gewinnt auf Grund der zunehmenden Komplexität technischer Systeme stetig an Bedeutung. Unter heterogenen Systemen versteht man technische Systeme, die aus analoger und digitaler Elektronik, aus Komponenten verschiedener physikalischer Domänen wie mechanischen Strukturen, thermischen und optischen Komponenten sowie aus Software bestehen können. Genügte es bisher, die einzelnen Komponenten für sich in ihrer eigenen Domäne mit einem speziellen Simulator zu simulieren, so ist es heute unerläßlich, auch die Interaktionen zwischen den Komponenten zu erfassen. Um solche Systeme mit einer einheitlichen Beschreibungsform erfassen zu können, entstand aus der digitalen Hardwarebeschreibungssprache VHDL die Systembeschreibungssprache VHDL-AMS. Bei der Modellierung eines Systems muß das tatsächliche Verhalten der Komponenten abstrahiert werden, um mathematisch erfaßbar und in begrenzter Zeit simulierbar zu sein. Der Grad der Abstraktion beeinflußt jedoch die Genauigkeit der Simulationsergebnisse wesentlich. Dabei muß bzw. kann das Verhalten in unterschiedlichen Komponenten unterschiedlich stark abstrahiert werden, um noch akzeptable Simulationsgenauigkeiten erzielen zu können. VHDL-AMS erlaubt die Beschreibung von Komponenten auf unterschiedlichen Abstraktionsniveaus. Man kann die unterschiedlich abstrakten Modelle der Komponenten aber nur schwer in einer Systemsimulation gemeinsam simulieren, da unterschiedlich abstrakte Modelle auch unterschiedlich abstrakte Schnittstellen aufweisen, so daß die Modelle nur mühsam miteinander verbunden werden können. Ein Austausch eines abstrakten Modells einer Komponente gegen ein weniger abstraktes Modell oder umgekehrt ist mit vielen fehleranfälligen und zeitaufwendigen Anpassungsschritten verbunden. Im Rahmen dieser Arbeit wird ein methodischer Ansatz vorgestellt, der es auf der Basis einer Vereinheitlichung der Modellschnittstellen ermöglicht, unterschiedlich abstrakte Modelle gemeinsam zu simulieren und einzelne Modelle gegen abstraktere oder weniger abstrakte Modelle ohne nennenswerten Zeit- und Modellierungsaufwand auszutauschen. Es werden die zu verwendenden Interfaceobjekte und Datentypen für digitale, analoge elektrische und nichtelektrische Schnittstellen unter VHDL-AMS und SystemC-AMS vorgestellt. Ebenso werden Methoden vorgestellt, die digitales, ereignisdiskretes Verhalten auf konservative elektrische Schnittstellen bzw. nichtkonservatives analoges Verhalten auf digitale Schnittstellen abbilden. Weiterhin wird erläutert, wie sich digitale Protokolle über Abstraktionsebenen hinweg übertragen lassen und ein modifizierter Top-Down Design-Flow vorgestellt. Die Demonstration der Anwendbarkeit der Methode erfolgt anhand eines Beispiels.
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Markert, Erik. "High-Level-Entwurf von Mikrosystemen." Doctoral thesis, Universitätsverlag der Technischen Universität Chemnitz, 2009. https://monarch.qucosa.de/id/qucosa%3A18216.

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Die Dissertationsschrift stellt eine Toolkette zum abstrakten Entwurf von Mikrosystemen vor. Mikrosysteme können aus Elementen verschiedener physikalischer Domänen bestehen und zusätzlich digitale Hardware sowie Software enthalten. Die Erfassung und Formalisierung dieser heterogenen Systeme stellt den ersten Schritt im Entwurfsprozess dar, die damit verbundene neue Methodik des Designs von Mikrosystemen bildet den Kern der vorliegenden Arbeit. Zur Erfassung der analogen Spezifikationsteile enthält die Arbeit die Schilderung und Implementierung neuer Datenstrukturen, die ausgehend von einer ausführlichen Anforderungsanalyse geschaffen wurden. Das abstrakte Systemverhalten wird mit Hilfe hybrider Automaten modelliert, die sowohl mit speziellen hybriden Werkzeugen als auch mit SystemC-AMS simulierbar sind. Darüber hinaus beschäftigt sich die Arbeit mit der Erfassung von Signalverläufen und Schaltplaninformationen. Die formalisierten Anforderungen ermöglichen erste Prüfungen der Spezifikation auf Konsistenz. Zur Unterstützung niedriger Abstraktionsebenen wie der Differentialgleichungsebene steht ein Wandler von SystemC-AMS nach VHDL-AMS bereit. In die Systembeschreibung mit SystemC-AMS ist die Definition und Verknüpfung von Kostenparametern integrierbar. Das daraus entstehende globale Gütemaß hilft dem Entwerferteam, die optimale Systemrealisierung zu finden.<br>The PhD thesis proposes a toolflow for the design of microsystems on higher abstraction levels. Microsystems may consist of components using effects in different physical domains plus additional digital hardware and software. The collection and formalization of these heterogeneous systems is a first step in the design process, the associated design method ist the key point of this work. The system behavior is modeled using hybrid automata, which are checkable using hybrid modelcheckers and simulable using SystemC-AMS. Furthermore the work deals with signal forms and circuit parameters. To support modeling on lower abstraction levels like differential algebraic equations a syntax conversion from SystemC-AMS to VHDL-AMS was included. The integration of cost factors into SystemC-AMS allows design space exploration during system simulation.
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Rostagni, Florent. "Classification morphologique d'un échantillon optique d'amas de galaxies." Thesis, Nice, 2014. http://www.theses.fr/2014NICE4055/document.

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Les amas de galaxies sont l'une des sondes cosmologiques permettant de contraindre les modèles d'univers, via leur fonction de masse et leur vitesse de croissance, elles-mêmes mesurées par l'état dynamique des amas. Les grands relevés présents et futurs permettent d'avoir accès à une information plus nombreuse et plus complète sur les amas de galaxies et donc d'utiliser de nouvelles méthodes de détermination de leur état dynamique. Dans cette thèse, une nouvelle méthode de caractérisation morphologique 2+1D des amas a été développée afin d'établir une nouvelle classification des amas. Il s'agit d'une méthode optique basée sur la position et la vitesse radiale des galaxies. Les structures dans la zone d'influence des amas sont détectées et caractérisées en projection et dans l'espace des vitesses radiales à l'aide d'une analyse en ondelettes. À partir du nombre de structures, les amas sont classés en amas unimodal, bimodal ou multimodal. L'ellipticité de leur distribution projetée et la gaussianité de la distribution des vitesses radiales sont également utilisées pour raffiner la classification. La méthode de caractérisation et de classification morphologique a été appliquée à un sous-échantillon de 403 amas issus du catalogue C4 en utilisant les données du SDSS. Il en est ressorti que 25% des amas sont unimodaux, 33% sont bimodaux et 42% sont multimodaux. Une analyse de la stabilité de la classification a également été réalisée ainsi qu'une comparaison avec les résultats de la littérature, que ce soit d'un point de vue statistique ou au niveau des amas individuels<br>Clusters of galaxies are one of the main cosmological probes used to constrain the cosmological parameters, through their mass function and their growth rate. The measure of these two quantities require the determination of the dynamical state of clusters. The present and future large and deep sky surveys give access to a more complete information on clusters and legitimate the development of new methods of determination of their dynamical state. In this thesis, a new method of characterization of the cluster morphology has been developed. It is a 2+1D method using galaxies and it enables to develop a new morphological classification of clusters. Structures around clusters are detected and characterized in projection and along the line of sight using a wavelet analysis. The new classification consists in counting the number of structures in the vicinity of clusters, three clusters classes were defined : unimodal, bimodal and multimodal. The ellipticity and the Gaussianity of the distribution of radial velocities are also used to refine the classification. The method was applied to a subsample of 403 clusters from the C4cluster catalogue using data from the SDSS. The results are : 25% of the clusters are unimodals, 33% are bimodals and 42% are multimodals. The stability of the classification with respect to the different parameters used was also performed as well as a comparison with the results from other studies in the literature
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Åberg, Ludvig. "Multimodal Classification of Second-Hand E-Commerce Ads." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233324.

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In second-hand e-commerce, categorization of new products is typically done by the seller. Automating this process makes it easier to upload ads and could lower the number of incorrectly categorized ads. Automatic ad categorization also makes it possible for a second-hand e-commerce platform to use a more detailed category system, which could make the shopping experience better for potential buyers. Product ad categorization is typically addressed as a text classification problem as most metadata associated with products are textual. By including image information, i.e. using a multimodal approach, better performance can however be expected. The work done in this thesis evaluates different multimodal deep learning models for the task of ad categorization on data from Blocket.se. We examine late fusion models, where the modalities are combined at decision level, and early fusion models, where the modalities are combined at feature level. We also introduce our own approach Text Based Visual Attention  (TBVA), which extends the image CNN Inception v3 with an attention mechanism to incorporate textual information. For all models evaluated, the text classifier fastText is used to process text data and the Inception v3 network to process image data. Our results show that the late fusion models perform best in our setting. We conclude that these models generally learn which of the baseline models to ’trust’, while early fusion and the TBVA models learn more abstract concepts. As future work, we would like to examine how the TBVA models perform on other tasks, such as ad similarity.<br>Produkter som läggs ut på marknadsplatser, såsom Blocket.se, kategoriseras oftast av säljaren själv. Att automatisera processen för kategorisering gör det därför både enklare och snabbare att lägga upp annonser och kan minska antalet produkter med felaktig kategori. Automatisk kategorisering gör det ocksåmöjligt för marknadsplatsen att använda ett mer detaljerat kategorisystem, vilket skulle kunna effektivisera sökandet efter produkter för potentiella köpare.Produktkategorisering adresseras ofta som ett klassificeringsproblem för text, eftersom den största delen av produktinformationen finns i skriftlig form. Genom att också inkludera produktbilder kan vi dock förvänta oss bättre resultat.I den här uppsatsen evalueras olika metoder för att använda både bild och text för annonsklassificering av data från blocket.se. I synnerhetundersökslate fusion modeller, där informationen från modaliteterna kombineras i samband med klassificeringen, samt early fusion modeller, där modaliteterna istället kombineras på en abstrakt nivå innan klassificeringen. Vi introduserar också vår egen modell Text Based Visual Attention (TBVA), en utvidgning av bildklassificeraren Inception v3 [1], som använder en attention mekanism för att inkorporera textinformation. För alla modeller som beskrivs i denna uppsats används textklassificeraren fast Text[2] för att processa text och bildklassificeraren Inception v3 för att processa bild. Våra resultat visar att late fusion modeller presterar bäst med vår data. I slutsatsen konstateras att late fusion modellerna lär sig vilka fall den ska 'lita' på text eller bild informationen, där early fusion och TBVA modellerna istället lär sig mer abstrakta koncept. Som framtida arbete tror vi det skulle vara av värde att undersöka hur TBVA modellerna presterar på andra uppgifter, såsom att bedöma likheter mellan annonser.
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Mendoza, Ivan. "Robustness in risk classification." Thesis, Connect to online version, 2008. http://proquest.umi.com/pqdweb?did=1675128161&sid=1&Fmt=2&clientId=10306&RQT=309&VName=PQD.

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Wood, Nicholas Linder. "A Novel Kernel-Based Classification Method using the Pythagorean Theorem." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1462522255.

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Liang, Fang. "Hyperplane-based classification techniques." Connect to online resource, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3284447.

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Cai, Lijuan. "Multilabel classification over category taxonomies." View abstract/electronic edition; access limited to Brown University users, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3318298.

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GOMES, FELIPE REIS. "PRODUCT OFFERING CLASSIFICATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2012. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=22577@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>Este trabalho apresenta o EasyLearn, um framework para apoiar o desenvolvimento de aplicações voltadas ao aprendizado supervisionado. O EasyLearn define uma camada intermediaria, de simples configuração e entendimento, entre a aplicação e o WEKA, um framework de aprendizado de máquina criado pela Universidade de Waikato. Todos os classificadores e filtros implementados pelo WEKA podem ser facilmente encapsulados para serem utilizados pelo EasyLearn. O EasyLearn recebe como entrada um conjunto de arquivos de configuração no formato XML contendo a definição do fluxo de processamento a ser executado, além da fonte de dados a ser processada, independente do formato. Sua saída é adaptável e pode ser configurada para produzir, por exemplo, relatórios de acurácia da classificação, a própria da fonte de dados classificada, ou o modelo de classificação já treinado. A arquitetura do EasyLearn foi definida após a análise detalhada dos processos de classificação, permitindo identificar inúmeras atividades em comum entre os três processos estudados aprendizado, avaliação e classificação). Através desta percepção e tomando as linguagens orientadas a objetos como inspiração, foi criado um framework capaz de comportar os processos de classificação e suas possíveis variações, além de permitir o reaproveitamento das configurações, através da implementação de herança e polimorfismo para os seus arquivos de configuração. A dissertação ilustra o uso do framework criado através de um estudo de caso completo sobre classificação de produtos do comércio eletrônico, incluindo a criação do corpus, engenharia de atributos e análise dos resultados obtidos.<br>This dissertation presents EasyLearn, a framework to support the development of supervised learning applications. EasyLearn dfines an intermediate layer, which is easy to configure and understand, between the application and WEKA, a machine learning framework created by the University of Waikato. All classifiers and filters implemented by WEKA can be easily encapsulated to be used by EasyLearn. EasyLearn receives as input a set of configuration files in XML format containing the definition of the processing flow to be executed, in addition to the data source to be classified, regardless of format. Its output is customizable and can be configured to produce classification accuracy reports, the classified data source, or the trained classification model. The architecture of EasyLearn was defined after a detailed analysis of the classification process, which identified a set of common activities among the three analyzed processes (learning, evaluation and classification). Through this insight and taking the object-oriented languages as inspiration, a framework was created which is able to support the classification processes and its variations, and which also allows reusing settings by implementing inheritance and polymorphism in their configuration files. This dissertation also illustrates the use of the created framework presenting a full case study about e-commerce product classification, including corpus creation, attribute engineering and result analysis.
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Books on the topic "AMS Classification"

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Chen, Teresa M. Verbal constructions and verbal classifications in Nataoran-Amis. Dept. of Linguistics, Research School of Pacific Studies, Australian National University, 1987.

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Ingetraut, Dahlberg, Gesellschaft für Klassifikation Jahrestagung, and Gesellschaft für Klassifikation Jahrestagung, eds. Klassifikation als Werkzeug der Lehre und Forschung. Indeks, 1986.

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C, McCormick Alexander, and Cox Rebecca D, eds. Classification systems for two-year colleges. Jossey-Bass, 2003.

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Klevenow, Gert-Holger. Klassifikation von Ausbildungsberufen als Basis für Berufsorientierung. Bundesanstalt für Arbeit, 2000.

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Niedersächsische Staats- und Universitätsbibliothek Göttingen, ed. Bibliothek als Archiv. Vandenhoeck & Ruprecht, 2007.

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Lopez, Damian. Seismic classification of military vehicles using statistical pattern recognition. UVB Universitätsverlag Dr. N. Brockmeyer, 1995.

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Porzio, Giovanni Camillo, Carla Rampichini, and Chiara Bocci, eds. CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS. Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-340-6.

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The book collects the short papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS). The meeting has been organized by the Department of Statistics, Computer Science and Applications of the University of Florence, under the auspices of the Italian Statistical Society and the International Federation of Classification Societies (IFCS). CLADAG is a member of the IFCS, a federation of national, regional, and linguistically-based classification societies. It is a non-profit, non-political scientific organization, whose aims are to further classification research.
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Dzhamay, Anton, Ken'ichi Maruno, and Christopher M. Ormerod. Algebraic and analytic aspects of integrable systems and painleve equations: AMS special session on algebraic and analytic aspects of integrable systems and painleve equations : January 18, 2014, Baltimore, MD. American Mathematical Society, 2015.

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Haslmayr, Hans-Peter. "Rote Liste" schützenswerter Bodenformen: Eine Methode zur Definition von schützenswerten Bodenformen als Planungsgrundlage flächenwirksamer Landnutzungen. Guthmann Peterson, 2011.

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Baldassarri, Fabrizio. Epistemic Practices and Plant Classification in Premodern European Botanical Knowledge. Amsterdam University Press, 2025. https://doi.org/10.5117/9789463728072.

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This volume aims to uncover the diverse approach to plants in the Renaissance and seventeenth century that paved the way for a definition of botany as a fully-fledged discipline. Its scope expands beyond the natural historical interest in collections and the fabrication of materia medica: moving from Varchi, Matthioli and Bauhin to Locke, Pinelli, and Linnaeus, among others, the contributions collected here connect practical and theoretical features, dealing with the challenges that characterized any involvement with plants. The authors focus on the linguistic shortcomings, problems in authenticating specimens, and efforts to establish new botanical geographies to favour trades, as well as the pursuits of new methodologies, artisanal technologies, and chymical experiments with plants. Botany thus emerges as a suitable discipline to disclose the complexities and challenges of early modern science in general.
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Book chapters on the topic "AMS Classification"

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Wynn, George W., and David P. Campbell. "Purchasing Tasks: A Classification Schemata." In Proceedings of the 1986 Academy of Marketing Science (AMS) Annual Conference. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11101-8_89.

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Morris, Michael H., and Peter L. Gillett. "The Distinctive Needs and Problems in Industrial Goods Classification." In Proceedings of the 1987 Academy of Marketing Science (AMS) Annual Conference. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17052-7_80.

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Lane, Paul M., and Jay D. Lindquist. "Definitions for The Fourth Dimension: A Proposed Time Classification System1." In Proceedings of the 1988 Academy of Marketing Science (AMS) Annual Conference. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17046-6_8.

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Perez-Rivera, Myra Mabel. "A Systematic Classification of Services to Gain Strategic Marketing Insights." In Proceedings of the 1994 Academy of Marketing Science (AMS) Annual Conference. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13162-7_105.

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Tang, Yujin, Kei Yonekawa, Mori Kurokawa, Shinya Wada, and Kiyohito Yoshihara. "Binary Classification of Sequences Possessing Unilateral Common Factor with AMS and APR." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93040-4_26.

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Green, Paul E., Jinho Kim, and Bruce Shandler. "Predictive Accuracy of Classification and Regression Trees (CART) Versus Conjoint Analysis." In Proceedings of the 1990 Academy of Marketing Science (AMS) Annual Conference. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13254-9_72.

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Warren, William E., and David L. Loudon. "Applying Lovelock’S Services Classification System to Residential Alternative Long-Distance Telephone Services." In Proceedings of the 1986 Academy of Marketing Science (AMS) Annual Conference. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11101-8_79.

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James, Kevin. "A Review and Update of the Classification of Goods System: A Customer Involvement System." In Proceedings of the 2009 Academy of Marketing Science (AMS) Annual Conference. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10864-3_43.

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Faria, A. J., John R. Dickinson, and Marc Schumacher. "A Test of the Level of Difficulty and Discriminating Ability of a Multiple Choice Question Classification Scheme." In Proceedings of the 1985 Academy of Marketing Science (AMS) Annual Conference. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16943-9_36.

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Lindquist, Jay D., and Paul M. Lane. "Exploring a Proposed Time Use/Commitment Classification Model: a Taxonomy and Methodology Extension to the Far East." In Proceedings of the 1992 Academy of Marketing Science (AMS) Annual Conference. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13248-8_34.

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Conference papers on the topic "AMS Classification"

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Liu, Yanghua, Rui Fu, Xiuhui Peng, and Yongxiao Tian. "AIS trajectory classification via improved IMM approach." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662118.

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Kharchenko, Sergey, and Sergey Kharchenko. "AN EXPERIMENT OF AUTOMATIC CLASSIFICATION AND MAPPING OF THE LANDFORMS OF THE YAMAL PENINSULA." In 24th SGEM International Multidisciplinary Scientific GeoConference 2024. STEF92 Technology, 2024. https://doi.org/10.5593/sgem2024/2.1/s08.13.

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This study investigates the potential of automated geomorphological mapping using geomorphometric analysis and machine learning on the Yamal Peninsula, Russia. The research aims to classify landforms based solely on geomorphometric characteristics, bypassing traditional manual interpretation of aerial imagery and digital elevation models (DEMs). The study utilized a DEM of the Yamal Peninsula and a reference geomorphological map, including 10 distinct landform types. A total of 119 geomorphometric variables, including spectral characteristics of the terrain, were calculated and used for training a Random Forest classifier. The results demonstrate that the model achieved a 65.4% overall accuracy, significantly exceeding the baseline accuracy of 10%. While some landforms, like the first river terrace, were accurately classified with 98% precision, others, such as floodplains, showed lower accuracy. The study identified key geomorphometric variables contributing to the classification, highlighting the importance of "focal" characteristics reflecting the texture and pattern of topographic dissection. The findings suggest that automated classification based on geomorphometric analysis holds promise for geomorphological mapping. It can be used to expedite the creation of geomorphological maps and assist in identifying areas of uncertainty for further investigation. However, future research is necessary to improve the accuracy of specific landform classifications, particularly those with high spatial variability.
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Ruffino, Samuele, Geethan Karunaratne, Michael Hersche, Luca Benini, Abu Sebastian, and Abbas Rahimi. "Zero-Shot Classification Using Hyperdimensional Computing." In 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2024. http://dx.doi.org/10.23919/date58400.2024.10546605.

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Képešiová, Zuzana, and Štefan Kozák. "An Effective Deep Learning Approach for Fault Detection and Classification of Levitation Processes." In 2025 Cybernetics & Informatics (K&I). IEEE, 2025. https://doi.org/10.1109/ki64036.2025.10916461.

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Badidová, Bianca, Radoslav Forgáč, Miloš Očkay, Martin Javurek, Peter Krammer, and Ladislav Hluchý. "A Dual-Camera Analysis of PCA Coefficients for Hyperspectral Classification of Tree Species." In 2025 Cybernetics & Informatics (K&I). IEEE, 2025. https://doi.org/10.1109/ki64036.2025.10916439.

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Sharma, Ravi, Prerna Varshney, Deepanshi Srivastava, and Km Bharati. "Analysis of Machine Learning Classifiers’ Efficacy for Social Media Ads Classification." In 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N). IEEE, 2024. https://doi.org/10.1109/icac2n63387.2024.10895494.

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Zhang, Hao, Fuhui Zhou, Qihui Wu, and Chau Yuen. "FSOS-AMC: Few-Shot Open-Set Learning for Automatic Modulation Classification." In 2024 16th International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2024. https://doi.org/10.1109/wcsp62071.2024.10826902.

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Sabo, Filip, and Aida Todri-Sanial. "ClassONN: Classification with Oscillatory Neural Networks Using the Kuramoto Model." In 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2024. http://dx.doi.org/10.23919/date58400.2024.10546829.

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Bhat, Nabeel Nisar, Rafael Berkvens, and Jeroen Famaey. "CSI4Free: GAN-Augmented mmWave CSI for Improved Pose Classification." In 2024 IEEE 4th International Symposium on Joint Communications & Sensing (JC&S). IEEE, 2024. http://dx.doi.org/10.1109/jcs61227.2024.10646223.

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Zhao, Kexin, Runqi Sui, Xuedong Wu, and Wenchuan Yang. "Intriguing Properties of Universal Adversarial Triggers for Text Classification." In 2024 5th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE). IEEE, 2024. http://dx.doi.org/10.1109/icbase63199.2024.10762239.

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Reports on the topic "AMS Classification"

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Whitt, Christine, Katherine Lacy, and Katherine Lim. America's farms and ranches at a glance. United States Department of Agriculture, Economic Research Service, 2023. http://dx.doi.org/10.32747/2023.8141996.ers.

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American farms represent a diverse set of business operations and farm operators. This annual report describes characteristics of U.S. farms and ranches with the most recent data from the Agricultural Resource Management Survey (ARMS), an annual survey conducted by USDA, National Agricultural Statistics Service (NASS) and USDA, Economic Research Service (ERS). Statistics are presented using a farm classification developed by USDA, ERS to categorize farms into groups with some common characteristics. The classifications used are mainly based on each farm's annual revenue, the main occupation of the farm's principal operator, and ownership (family versus nonfamily). Additionally, the report explores the differences in farm operations in 2022 by race and ethnicity of the operators
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Han, Euihong, and George Karypis. Centroid-Based Document Classification Algorithms: Analysis & Experimental Results. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada439538.

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Pilania, Ghanshyam, James E. Gubernatis, Turab Lookman, and Rampi Ramprasad. Materials Classification & Accelerated Property Predictions using Machine Learning. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1184607.

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Evans, Julie, Kendra Sikes, and Jamie Ratchford. Vegetation classification at Lake Mead National Recreation Area, Mojave National Preserve, Castle Mountains National Monument, and Death Valley National Park: Final report (Revised with Cost Estimate). National Park Service, 2020. http://dx.doi.org/10.36967/nrr-2279201.

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Vegetation inventory and mapping is a process to document the composition, distribution and abundance of vegetation types across the landscape. The National Park Service’s (NPS) Inventory and Monitoring (I&amp;M) program has determined vegetation inventory and mapping to be an important resource for parks; it is one of 12 baseline inventories of natural resources to be completed for all 270 national parks within the NPS I&amp;M program. The Mojave Desert Network Inventory &amp; Monitoring (MOJN I&amp;M) began its process of vegetation inventory in 2009 for four park units as follows: Lake Mead National Recreation Area (LAKE), Mojave National Preserve (MOJA), Castle Mountains National Monument (CAMO), and Death Valley National Park (DEVA). Mapping is a multi-step and multi-year process involving skills and interactions of several parties, including NPS, with a field ecology team, a classification team, and a mapping team. This process allows for compiling existing vegetation data, collecting new data to fill in gaps, and analyzing the data to develop a classification that then informs the mapping. The final products of this process include a vegetation classification, ecological descriptions and field keys of the vegetation types, and geospatial vegetation maps based on the classification. In this report, we present the narrative and results of the sampling and classification effort. In three other associated reports (Evens et al. 2020a, 2020b, 2020c) are the ecological descriptions and field keys. The resulting products of the vegetation mapping efforts are, or will be, presented in separate reports: mapping at LAKE was completed in 2016, mapping at MOJA and CAMO will be completed in 2020, and mapping at DEVA will occur in 2021. The California Native Plant Society (CNPS) and NatureServe, the classification team, have completed the vegetation classification for these four park units, with field keys and descriptions of the vegetation types developed at the alliance level per the U.S. National Vegetation Classification (USNVC). We have compiled approximately 9,000 existing and new vegetation data records into digital databases in Microsoft Access. The resulting classification and descriptions include approximately 105 alliances and landform types, and over 240 associations. CNPS also has assisted the mapping teams during map reconnaissance visits, follow-up on interpreting vegetation patterns, and general support for the geospatial vegetation maps being produced. A variety of alliances and associations occur in the four park units. Per park, the classification represents approximately 50 alliances at LAKE, 65 at MOJA and CAMO, and 85 at DEVA. Several riparian alliances or associations that are somewhat rare (ranked globally as G3) include shrublands of Pluchea sericea, meadow associations with Distichlis spicata and Juncus cooperi, and woodland associations of Salix laevigata and Prosopis pubescens along playas, streams, and springs. Other rare to somewhat rare types (G2 to G3) include shrubland stands with Eriogonum heermannii, Buddleja utahensis, Mortonia utahensis, and Salvia funerea on rocky calcareous slopes that occur sporadically in LAKE to MOJA and DEVA. Types that are globally rare (G1) include the associations of Swallenia alexandrae on sand dunes and Hecastocleis shockleyi on rocky calcareous slopes in DEVA. Two USNVC vegetation groups hold the highest number of alliances: 1) Warm Semi-Desert Shrub &amp; Herb Dry Wash &amp; Colluvial Slope Group (G541) has nine alliances, and 2) Mojave Mid-Elevation Mixed Desert Scrub Group (G296) has thirteen alliances. These two groups contribute significantly to the diversity of vegetation along alluvial washes and mid-elevation transition zones.
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JANICEK, G. P. Sub system & component level safety classification evaluation & identification for tank farm safety systems. Office of Scientific and Technical Information (OSTI), 2001. http://dx.doi.org/10.2172/807460.

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JANICEK, G. P. Sub system & component level safety classification evaluation & identification for tank farm safety systems [sec 1 & 2]. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/807666.

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J.A. Ziegler. CLASSIFICATION OF THE MGR SITE-GENERATED HAZAROUS NONHAZARDOUS & SANITARY WASTE DISPOSAL SYSTEM. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/860249.

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Rodriguez, Dirk, and Cameron Williams. Channel Islands National Park: Vegetation classification and mapping project report. National Park Service, 2025. https://doi.org/10.36967/2311434.

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In 2012, Channel Islands National Park (CHIS), with support and funding from the National Park Service’s (NPS) National Vegetation Mapping Inventory (VMI) Program, initiated new vegetation classifications and maps for four northern Channel Islands—Anacapa Island (ANI), Santa Rosa Island (SRI), Santa Cruz Island (SCI), and San Miguel Island (SMI). The project was conducted in two distinct phases. Phase 1 consisted of a mapping and classification effort for ANI, SRI, and SMI. Phase 2 mapped and classified the vegetation for SCI alone. Initial site visits and reconnaissance were conducted jointly by AIS and NPS, followed by NPS-led field data collection efforts. Data collection for the photo interpretation and for the basis of the vegetation classification continued for more than two years, resulting in the completion of over 1,600 field plots and rapid field assessments. For both Phases 1 and 2, map polygons were delineated from photo interpretation of 2012 and 2017 (respectively) aerial color imagery (six-inch resolution) using heads-up digitizing. In total, Phase 1 generated 13,767 polygons that were delineated across 84 different mapping units and Phase 2 generated 13,901 polygons that were delineated across 52 different mapping units. Once draft maps were completed, accuracy assessments were completed to ensure polygons were mapped accurately. The accuracy assessment analysis revealed some discrepancies; however, further review yielded no change to the original field identifications. For Phases 1 and 2 combined, analysis of the field data resulted in 58 plant alliances and 114 associations. SRI supported the highest number of vegetation types despite being the second largest Channel Island, whereas the largest island, SCI, ranked second. However, SCI had the most shrub-dominated vegetation types, whereas SRI had the most herb-dominated types. Six alliances/associations were shared among all four islands, 17 were shared by three islands, 39 were shared by two islands, and another 39 were unique to individual islands.
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Aarup, Thorkild, Chadi Abdallah, Bernadette Abela-Ridder, et al. Hazard Information Profiles: Supplement to UNDRR-ISC Hazard Definition & Classification Review – Technical Report. International Science Council, 2021. https://doi.org/10.24948/2021.05.

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Chien, Stanley, Yaobin Chen, Lauren Christopher, Mei Qiu, and Zhengming Ding. Road Condition Detection and Classification from Existing CCTV Feed. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317364.

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The Indiana Department of Transportation (INDOT) has approximately 500 digital cameras along highways in populated areas of Indiana. These cameras are used to monitor traffic conditions around the clock, all year round. Currently, the videos from these cameras are observed one-by-one by human operators looking for traffic conditions and incidents. The main objective of this research was to develop an automatic, real-time system to monitor traffic conditions and detect incidents automatically. The Transportation and Autonomous Systems Institute (TASI) of the Purdue School of Engineering and Technology at Indiana University-Purdue University Indianapolis (IUPUI) and the Traffic Management Center of INDOT developed a system that monitors the traffic conditions based on the INDOT CCTV video feeds. The proposed system performs traffic flow estimation, incident detection, and classification of vehicles involved in an incident. The research team designed the system, including the hardware and software components added to the existing INDOT CCTV system; the relationship between the added system and the currently existing INDOT system; the database structure for traffic data extracted from the videos; and a user-friendly, web-based server for showing the incident locations automatically. The specific work in this project includes vehicle-detection, road boundary detection, lane detection, vehicle count over time, flow-rate detection, traffic condition detection, database development, web-based graphical user interface (GUI), and a hardware specification study. The preliminary prototype of some system components has been implemented in the Development of Automated Incident Detection System Using Existing ATMS CCT (SPR-4305).
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