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Thematische Bibliographien / Classification

Auswahl der wissenschaftlichen Literatur zum Thema „Classification“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 22. Juni 2024

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Inhaltsverzeichnis

Zeitschriftenartikel
Dissertationen
Bücher
Buchteile
Konferenzberichte
Berichte der Organisationen

Zeitschriftenartikel zum Thema "Classification":

1

Thomas, Pravin, Anand Kumar, Ahamed Subir, Brian E. McGeeney, Madhav Raje, Divyani Garg, Chaithra D. Aroor, Arunmozhimaran Elavarasi und Kris Castle. „Classification of Head, Neck, and Face Pains First Edition (WHS-MCH1): Position paper of the WHS Classification Committee“. Headache Medicine Connections 1, Nr. 1 (20.08.2021): 1–108. http://dx.doi.org/10.52828/hmc.v1i1.classifications.

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2

Willatt, D. J., M. S. McCormick, R. P. Morton und P. M. Stell. „Staging of Maxillary Cancer“. Annals of Otology, Rhinology & Laryngology 96, Nr. 2 (März 1987): 137–41. http://dx.doi.org/10.1177/000348948709600201.

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Of the many proposed classifications for staging maxillary sinus cancer, none has been adopted universally and none is known to be superior to the others. This study identified the best of six currently used classifications using data from 53 previously untreated patients with squamous cell carcinoma of the maxillary sinus. Analysis of each classification's ability to stage the majority of patients, produce a balanced distribution of T stages, and correlate T stage with treatment and prognosis revealed Harrison's classification to be the best. Harrison's classification should be adopted worldwide as the classification of choice for staging squamous cell carcinoma of the maxillary sinus.

3

Jacob, Elin K. „Proposal for a Classification of Classifications built on Beghtol’s Distinction between “Naïve Classification” and “Professional Classification”“. KNOWLEDGE ORGANIZATION 37, Nr. 2 (2010): 111–20. http://dx.doi.org/10.5771/0943-7444-2010-2-111.

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4

Feleke, Tekabe Legesse. „Ethiosemitic languages: Classifications and classification determinants“. Ampersand 8 (2021): 100074. http://dx.doi.org/10.1016/j.amper.2021.100074.

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5

Dozic, Slobodan, Dubravka Cvetkovic-Dozic, Milica Skender-Gazibara und Branko Dozic. „Review of the World Health Organization classification of tumors of the nervous system“. Archive of Oncology 10, Nr. 3 (2002): 175–77. http://dx.doi.org/10.2298/aoo0203175d.

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(Conclusion) Classifications of the nervous system tumors should be neither static nor definitive. The most recent, third, current WHO classification of nervous system tumors was published in 2000. Many substantial changes were introduced. New entities include the chordoid glioma of the third ventricle, the atypical teratoid/rhabdoid tumor, cerebellar liponeurocytoma (the former lipomatous medulloblstoma of the cerebellum), solitary fibrous tumor and perineurioma. The new tumor variants include the large cell medulloblastoma, tanacytic ependymoma and rhabdoid meningioma. Several essential changes were introduced in the meningiomas regarding histological subtypes, grading and proliferation index. In addition to new entities described in the 2000 WHO classification there are newly brain tumor entities and tumor variants, which are not included in the current classification due to the insufficient number of reporeted cases, for example papillary glioneuronal tumor, rosetted glioneuronal tumor, lipoastrocytoma and lipomatous meningioma. They will be probably accepted in the next WHO classificaton. In the current WHO classification the importance of cytogenetic and molecular biologic investigation in the understanding and further classifications of these tumors has been emphasized.

6

Vu, Catphuong, und David Gendelberg. „Classifications in Brief: AO Thoracolumbar Classification System“. Clinical Orthopaedics & Related Research 478, Nr. 2 (09.12.2019): 434–40. http://dx.doi.org/10.1097/corr.0000000000001086.

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7

Fedorova, Natalia. „BASIC CLASSIFIERS OF FORMAL CLASSIFICATION THEORY OF TECHNICAL SYSTEMS: HIERARCHIES, VECTORS AND MATRICES, BANDS“. Vestnik of Astrakhan State Technical University. Series: Management, computer science and informatics 2021, Nr. 3 (30.07.2021): 28–40. http://dx.doi.org/10.24143/2072-9502-2021-3-28-40.

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The article considers the importance of a technical system among other technical systems in order to ensure its functioning and development, to classify objects, subjects, processes of the technical and related systems. Previously, the author presented the basics of the formal classification theory. This article describes the basic classifiers and operations with them. Three types of basic classifications are identified: discrete hierarchical, discrete matrix and continuous band classifications. For them the concept, structure, dimension, basic operations (addition, multiplication, equality) are defined. In the hierarchy, the classification attributes can be sorted by subordination, when the classification attributes of the lower levels of the hierarchy detail the features of higher levels. The dimension of the hierarchical classification is the number of levels of classification features. Matrix classifications (including vector and super-matrix) are used when the classification attributes are equal and their values are discrete. Band classifications are similar in structure to matrix classifications, but the value of the classification attribute is the interval of numbers, for which the lower and upper boundaries are determined. The dimension of the matrix and band classifications is equal to the number of non-subordinate classification attributes. For all classifications, multiplication is equivalent to the introduction of new classification attributes, addition is the introduction of new values of already existing classification attributes. A unified approach to various types of classifications makes it possible to plan the structure of classifications of specific technical systems, taking into account the properties of characteristic parameters

8

Di Lauro, Salvatore, Mustafa R. Kadhim, David G. Charteris und J. Carlos Pastor. „Classifications for Proliferative Vitreoretinopathy (PVR): An Analysis of Their Use in Publications over the Last 15 Years“. Journal of Ophthalmology 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/7807596.

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Purpose. To evaluate the current and suitable use of current proliferative vitreoretinopathy (PVR) classifications in clinical publications related to treatment.Methods. A PubMed search was undertaken using the term “proliferative vitreoretinopathy therapy”. Outcome parameters were the reported PVR classification and PVR grades. The way the classifications were used in comparison to the original description was analyzed. Classification errors were also included. It was also noted whether classifications were used for comparison before and after pharmacological or surgical treatment.Results. 138 papers were included. 35 of them (25.4%) presented no classification reference or did not use any one. 103 publications (74.6%) used a standardized classification. The updated Retina Society Classification, the first Retina Society Classification, and the Silicone Study Classification were cited in 56.3%, 33.9%, and 3.8% papers, respectively. Furthermore, 3 authors (2.9%) used modified-customized classifications and 4 (3.8%) classification errors were identified. When the updated Retina Society Classification was used, only 10.4% of authors used a full C grade description. Finally, only 2 authors reported PVR grade before and after treatment.Conclusions. Our findings suggest that current classifications are of limited value in clinical practice due to the inconsistent and limited use and that it may be of benefit to produce a revised classification.

9

Fortune, Nicola, Stephanie Short und Richard Madden. „Building a statistical classification: A new tool for classification development and testing“. Statistical Journal of the IAOS 36, Nr. 4 (25.11.2020): 1213–21. http://dx.doi.org/10.3233/sji-200633.

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Statistical classifications are essential for collecting consistent data that can be compared over space and time. However, a publicly-documented body of practice concerning how to undertake the development and testing of a statistical classification is currently lacking. What aspects of the classification should be tested during the development process? How do we judge whether the classification is fit-for-purpose? How should problems and shortcomings be identified so that they can be remedied? To fill this gap, we drew on existing, authoritative sources to develop an analytic structure for use in the development and testing of statistical classifications. It consists of two components: (1) a statistical classification development and testing framework reflecting the required features of a statistical classification; and (2) a 4-tier model representing the main elements that make up a statistical classification, to use as a heuristic structure within which to locate issues identified and consider how they can be addressed. In this paper, we outline the development of the framework and model, and reflect on their application in testing a draft classification of health interventions. We propose this analytic structure as a new tool to support those engaged in the development of statistical classifications.

10

Yaroshynskyi, M. S., O. V. Sirotkin, D. P. Sinko, S. B. Hunko und D. O. Manoliuk. „Correctness of Flat Classification“. Èlektronnoe modelirovanie 45, Nr. 2 (24.04.2023): 34–43. http://dx.doi.org/10.15407/emodel.45.02.034.

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Classifications are widely used in semantic networks and decision support systems based on formal knowledge and are part of computer ontologies. Classifications and computer ontologies built on them are the result of the work of one or more experts. As a result, such classifications reflect the subjective view of the author or authors on the world and the relationship between the classes (concepts) of the created classification. In the work, the authors propose an approach that will allow assessing how correctly the classification is constructed.

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Dissertationen zum Thema "Classification":

1

Bogers, Toine, Willem Thoonen und den Bosch Antal van. „Expertise classification: Collaborative classification vs. automatic extraction“. dLIST, 2006. http://hdl.handle.net/10150/105709.

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Social classification is the process in which a community of users categorizes the resources in that community for their own use. Given enough users and categorization, this will lead to any given resource being represented by a set of labels or descriptors shared throughout the community (Mathes, 2004). Social classification has become an extremely popular way of structuring online communities in recent years. Well-known examples of such communities are the bookmarking websites Furl (http://www.furl.net/) and del.icio.us (http://del.icio.us/), and Flickr (http://www.flickr.com/) where users can post their own photos and tag them. Social classification, however, is not limited to tagging resources: another possibility is to tag people, examples of which are Consumating (http://www.consumating.com/), a collaborative tag-based personals website, and Kevo (http://www.kevo.com/), a website that lets users tag and contribute media and information on celebrities. Another application of people tagging is expertise classification, an emerging subfield of social classification. Here, members of a group or community are classified and ranked based on the expertise they possess on a particular topic. Expertise classification is essentially comprised of two different components: expertise tagging and expert ranking. Expertise tagging focuses on describing one person at a time by assigning tags that capture that person's topical expertise, such as â speech recognition' or â small-world networks'. information request, such as, for instance, a query submitted to a search engine. Methods are developed to combine the information about individual members' expertise (tags), to provide on-the-fly query-driven rankings of community members. Expertise classification can be done in two principal ways. The simplest option follows the principle of social bookmarking websites: members are asked to supply tags that describe their own expertise and to rank the other community members with regard to a specific request for information. Alternatively, automatic expertise classification ideally extracts expertise terms automatically from a user's documents and e-mails by looking for terms that are representative for that user. These terms are then matched on the information request to produce an expert ranking of all community members. In this paper we describe such an automatic method of expertise classification and evaluate it using human expertise classification judgments. In the next section we will describe some of the related work on expertise classification, after which we will describe our automatic method of expertise classification and our evaluation of them in sections 3 and 4. Sections 5.1 and 5.1 describe our findings on expertise tagging and expert rankings, followed by discussion and our conclusions in section 6 and recommendations for future work in section 7.

2

Ravindra, Dilip. „Firmware and classification algorithm development for vehicle classification“. Thesis, California State University, Long Beach, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1603749.

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Vehicle classification is one of the active research topic in Intelligent Transport System. This project proposes an approach to classify the vehicles on freeway with respect to the size of the vehicle. This vehicle classification is based on threshold based algorithm. This system consists of two AMR magneto-resistive sensors connected to TI msp430 development board. The data collected from the two magneto resistive sensors is analyzed and supplied to threshold based algorithm to differentiate the vehicles. With the use of minimum number features extracted from the data it was possible to produce very efficient algorithm that is capable of differentiating the vehicles.

3

Phillips, Rhonda D. „A Probabilistic Classification Algorithm With Soft Classification Output“. Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/26701.

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This thesis presents a shared memory parallel version of the hybrid classification algorithm IGSCR (iterative guided spectral class rejection), a novel data reduction technique that can be used in conjunction with PIGSCR (parallel IGSCR), a noise removal method based on the maximum noise fraction (MNF), and a continuous version of IGSCR (CIGSCR) that outputs soft classifications. All of the above are either classification algorithms or preprocessing algorithms necessary prior to the classification of high dimensional, noisy images. PIGSCR was developed to produce fast and portable code using Fortran 95, OpenMP, and the Hierarchical Data Format version 5 (HDF5) and accompanying data access library. The feature reduction method introduced in this thesis is based on the singular value decomposition (SVD). This feature reduction technique demonstrated that SVD-based feature reduction can lead to more accurate IGSCR classifications than PCA-based feature reduction. This thesis describes a new algorithm used to adaptively filter a remote sensing dataset based on signal-to-noise ratios (SNRs) once the maximum noise fraction (MNF) has been applied. The adaptive filtering scheme improves image quality as shown by estimated SNRs and classification accuracy improvements greater than 10%. The continuous iterative guided spectral class rejection (CIGSCR) classification method is based on the iterative guided spectral class rejection (IGSCR) classification method for remotely sensed data. Both CIGSCR and IGSCR use semisupervised clustering to locate clusters that are associated with classes in a classification scheme. This type of semisupervised classification method is particularly useful in remote sensing where datasets are large, training data are difficult to acquire, and clustering makes the identification of subclasses adequate for training purposes less difficult. Experimental results indicate that the soft classification output by CIGSCR is reasonably accurate (when compared to IGSCR), and the fundamental algorithmic changes in CIGSCR (from IGSCR) result in CIGSCR being less sensitive to input parameters that influence iterations.
Ph. D.

4

Матусевич, Олександр Павлович. „Classification Fonts“. Thesis, Київський національний університет технологій та дизайну, 2017. https://er.knutd.edu.ua/handle/123456789/7344.

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5

Ярмак, Любов Павлівна, Любовь Павловна Ярмак, Liubov Pavlivna Yarmak, Оксана Робертівна Гладченко, Оксана Робертовна Гладченко und Oksana Robertivna Hladchenko. „Test classification“. Thesis, Сумський державний університет, 2014. http://essuir.sumdu.edu.ua/handle/123456789/34677.

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We will outline here rather briefly some of the ways tests can be classified. Understanding contrasting exam types can be helpful to teachers since tests of one kind may not always be successfully substituted for those of another kind. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/34677

6

Taylor, Paul Clifford. „Classification trees“. Thesis, University of Bath, 1990. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306312.

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7

Bonneau, Jean-Christophe. „La classification des contrats : essai d'une analyse systémique des classifications du Code civil“. Grenoble, 2010. http://www.theses.fr/2010GREND017.

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La classification des contrats telle qu'elle est énoncée aux articles 1102 et suivants du Code civil se distingue structurellement des classifications modernes lui ayant été ajoutées. Prenant au sérieux l'idée d’une approche globale de la classification, les classifications du Code civil, séparées d'un régime juridique qui ne dépend pas en réalité d'elles et de notions qui lui sont étrangères, comme la cause, ont été envisagées dans leurs rapports de logique et de complémentarité. L'existence des chaînes de classifications, nouvelle classification résultant de l'assemblage cohérent des différentes classifications prévues par le Code civil, a pu être révélée au terme d'une étude visant à comprendre comment ces classifications se lient et se combinent entre elles. Les fonctionnalités de la classification des contrats ont alors été déduites de la structure même des classifications du Code civil réunies en chaînes. Celles-ci ont pour propriété de révéler ce qui constitue l'essence du contrat, en permettant de le distinguer de certaines figures qui tentent de s'y assimiler mais s'en distinguent néanmoins dès lors que l'aptitude d'un objet juridique à s'intégrer dans les chaînes de classifications est perçu comme conditionnant la qualification contractuelle elle-même. Envisagées comme un critère privilégié de définition du contrat, qui peut inspirer les projets visant à élaborer un droit européen des contrats, les chaînes de classifications ont ensuite été pensées dans leurs rapports avec la diversité des contrats nommés. Les chaînes de classifications absorbent ces derniers ainsi que leur régime juridique qui peut, en conséquence, être transposé aux contrats innomés. Permettant un renouvellement des regroupements et des distinctions généralement perçus, les chaînes de classifications apportent un éclairage nouveau au processus de qualification du contrat, contribuent à préciser le domaine de la modification du contrat, et fournissent, enfin, un fondement à l'action contractuelle directe qui s'exerce dans les chaînes de contrats
The classification of contracts as it is stated in the civil Code articles 1102 onwards structurally distinguishes itself from modern classifications having been added to it. Looking thoroughly at the matter of a global approach of classification, the classifications of the civil Code, separated from a legal regime which does not in fact depend on them and on notions which are foreign to it, such as the concept of “cause”, were considered in their connections of logic and complementarity. The existence of the chains of classifications, a new classification resulting from the coherent assembly of the various classifications provided for the civil Code, were brought to light thanks to a study aiming at understanding how these classifications are bound and harmonized. The features of the classification of contracts were then deducted from the very structure of the classifications of the civil Code combined in chains. These have for feature to reveal what constitutes the essence of the contract, by allowing to distinguish it from certain figures which try to assimilate to it but nevertheless distinguish themselves from it since the capacity of a legal object to become integrated into the chains of classifications is perceived as conditional on the contractual qualification itself. Considered as a preferred criterion of the definition of the contract, which can give rise to projects aiming at the elaboration of a body of European contract laws, the chains of classifications were then conceptualised in their connections with the variety of the named contracts. The chains of classifications absorb these contracts as well as their legal regime which can, consequently, be transposed into the unnamed contracts. Allowing a renewal of the groupings generally perceived, the chains of classifications bring a new light to the process of qualification of the contract. They contribute to specify the domain of the modification of the contract, and finally supply a foundation for the direct contractual action which is applied to the chains of contracts

8

Van, der Westhuizen Cornelius Stephanus. „Nearest hypersphere classification : a comparison with other classification techniques“. Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/95839.

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Thesis (MCom)--Stellenbosch University, 2014.
ENGLISH ABSTRACT: Classification is a widely used statistical procedure to classify objects into two or more classes according to some rule which is based on the input variables. Examples of such techniques are Linear and Quadratic Discriminant Analysis (LDA and QDA). However, classification of objects with these methods can get complicated when the number of input variables in the data become too large (􀝊 ≪ 􀝌), when the assumption of normality is no longer met or when classes are not linearly separable. Vapnik et al. (1995) introduced the Support Vector Machine (SVM), a kernel-based technique, which can perform classification in cases where LDA and QDA are not valid. SVM makes use of an optimal separating hyperplane and a kernel function to derive a rule which can be used for classifying objects. Another kernel-based technique was proposed by Tax and Duin (1999) where a hypersphere is used for domain description of a single class. The idea of a hypersphere for a single class can be easily extended to classification when dealing with multiple classes by just classifying objects to the nearest hypersphere. Although the theory of hyperspheres is well developed, not much research has gone into using hyperspheres for classification and the performance thereof compared to other classification techniques. In this thesis we will give an overview of Nearest Hypersphere Classification (NHC) as well as provide further insight regarding the performance of NHC compared to other classification techniques (LDA, QDA and SVM) under different simulation configurations. We begin with a literature study, where the theory of the classification techniques LDA, QDA, SVM and NHC will be dealt with. In the discussion of each technique, applications in the statistical software R will also be provided. An extensive simulation study is carried out to compare the performance of LDA, QDA, SVM and NHC for the two-class case. Various data scenarios will be considered in the simulation study. This will give further insight in terms of which classification technique performs better under the different data scenarios. Finally, the thesis ends with the comparison of these techniques on real-world data.
AFRIKAANSE OPSOMMING: Klassifikasie is ’n statistiese metode wat gebruik word om objekte in twee of meer klasse te klassifiseer gebaseer op ’n reël wat gebou is op die onafhanklike veranderlikes. Voorbeelde van hierdie metodes sluit in Lineêre en Kwadratiese Diskriminant Analise (LDA en KDA). Wanneer die aantal onafhanklike veranderlikes in ’n datastel te veel raak, die aanname van normaliteit nie meer geld nie of die klasse nie meer lineêr skeibaar is nie, raak die toepassing van metodes soos LDA en KDA egter te moeilik. Vapnik et al. (1995) het ’n kern gebaseerde metode bekendgestel, die Steun Vektor Masjien (SVM), wat wel vir klassifisering gebruik kan word in situasies waar metodes soos LDA en KDA misluk. SVM maak gebruik van ‘n optimale skeibare hipervlak en ’n kern funksie om ’n reël af te lei wat gebruik kan word om objekte te klassifiseer. ’n Ander kern gebaseerde tegniek is voorgestel deur Tax and Duin (1999) waar ’n hipersfeer gebruik kan word om ’n gebied beskrywing op te stel vir ’n datastel met net een klas. Dié idee van ’n enkele klas wat beskryf kan word deur ’n hipersfeer, kan maklik uitgebrei word na ’n multi-klas klassifikasie probleem. Dit kan gedoen word deur slegs die objekte te klassifiseer na die naaste hipersfeer. Alhoewel die teorie van hipersfere goed ontwikkeld is, is daar egter nog nie baie navorsing gedoen rondom die gebruik van hipersfere vir klassifikasie nie. Daar is ook nog nie baie gekyk na die prestasie van hipersfere in vergelyking met ander klassifikasie tegnieke nie. In hierdie tesis gaan ons ‘n oorsig gee van Naaste Hipersfeer Klassifikasie (NHK) asook verdere insig in terme van die prestasie van NHK in vergelyking met ander klassifikasie tegnieke (LDA, KDA en SVM) onder sekere simulasie konfigurasies. Ons gaan begin met ‘n literatuurstudie, waar die teorie van die klassifikasie tegnieke LDA, KDA, SVM en NHK behandel gaan word. Vir elke tegniek gaan toepassings in die statistiese sagteware R ook gewys word. ‘n Omvattende simulasie studie word uitgevoer om die prestasie van die tegnieke LDA, KDA, SVM en NHK te vergelyk. Die vergelyking word gedoen vir situasies waar die data slegs twee klasse het. ‘n Verskeidenheid van data situasies gaan ook ondersoek word om verdere insig te toon in terme van wanneer watter tegniek die beste vaar. Die tesis gaan afsluit deur die genoemde tegnieke toe te pas op praktiese datastelle.

9

Olin, Per. „Evaluation of text classification techniques for log file classification“. Thesis, Linköpings universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166641.

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System log files are filled with logged events, status codes, and other messages. By analyzing the log files, the systems current state can be determined, and find out if something during its execution went wrong. Log file analysis has been studied for some time now, where recent studies have shown state-of-the-art performance using machine learning techniques. In this thesis, document classification solutions were tested on log files in order to classify regular system runs versus abnormal system runs. To solve this task, supervised and unsupervised learning methods were combined. Doc2Vec was used to extract document features, and Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) based architectures on the classification task. With the use of the machine learning models and preprocessing techniques the tested models yielded an f1-score and accuracy above 95% when classifying log files.

10

Anteryd, Fredrik. „Information Classification in Swedish Governmental Agencies : Analysis of Classification Guidelines“. Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-11493.

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Information classification deals with the handling of sensitive information, such as patient records and social security information. It is of utmost importance that this information is treated with caution in order to ensure its integrity and security. In Sweden, the Civil Contingencies Agency has established a set of guidelines for how governmental agencies should handle such information. However, there is a lack of research regarding how well these guidelines are followed as well as if the agencies have made accommodations of these guidelines of their own. This work presents the results from a survey sent to 245 governmental agencies in Sweden, investigating how information classification actually is performed today. The questionnaire was answered by 144 agencies and 54 agencies provided detailed documents of their classification process. The overall results show that the classification process is difficult, while those who provided documents proved to have good guidelines, but not always consistent with the existing recommendations.

Mehr Quellen

Bücher zum Thema "Classification":

1

Library of Congress. Subject Cataloging Division. Classification. 3. Aufl. Washington, D.C: The Library, 1989.

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2

Library of Congress. Subject Cataloging Division. Classification. Washington: The Library, 1988.

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3

Sabzwari, Ghaniul Akram. Classification. Karachi: s.n., 2005.

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4

Gordon, A. D. Classification. 2. Aufl. Boca Raton: Chapman & Hall/CRC, 1999.

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5

Library of Congress. Cataloging Policy and Support Office. Classification. Washington: Library of Congress, 1993.

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6

Library of Congress. Office for Subject Cataloging Policy. Classification. 5. Aufl. Washington, DC: Library of Congress, 1992.

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7

Haroon, Mohammed. Music classification: Schedule for colon classification. New Delhi: Kanishka Publishers, Distributors, 2010.

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8

Haroon, Mohammed. Music classification: Schedule for colon classification. New Delhi: Kanishka Publishers, Distributors, 2010.

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9

Haroon, Mohammed. Music classification: Schedule for colon classification. New Delhi: Kanishka Publishers, Distributors, 2010.

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Baba, Abdul Majid. Dewey decimal classification, Universal decimal classification, and Colon classification: Development, structure, comparison. Srinagar, Kashmir: Gulshan Publishers, 1988.

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Mehr Quellen

Buchteile zum Thema "Classification":

1

Herrera, Francisco, Francisco Charte, Antonio J. Rivera und María J. del Jesus. „Multilabel Classification“. In Multilabel Classification, 17–31. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41111-8_2.

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Herrera, Francisco, Francisco Charte, Antonio J. Rivera und María J. del Jesus. „Introduction“. In Multilabel Classification, 1–16. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41111-8_1.

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Herrera, Francisco, Francisco Charte, Antonio J. Rivera und María J. del Jesus. „Case Studies and Metrics“. In Multilabel Classification, 33–63. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41111-8_3.

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Herrera, Francisco, Francisco Charte, Antonio J. Rivera und María J. del Jesus. „Transformation-Based Classifiers“. In Multilabel Classification, 65–79. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41111-8_4.

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Herrera, Francisco, Francisco Charte, Antonio J. Rivera und María J. del Jesus. „Adaptation-Based Classifiers“. In Multilabel Classification, 81–99. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41111-8_5.

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Herrera, Francisco, Francisco Charte, Antonio J. Rivera und María J. del Jesus. „Ensemble-Based Classifiers“. In Multilabel Classification, 101–13. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41111-8_6.

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Herrera, Francisco, Francisco Charte, Antonio J. Rivera und María J. del Jesus. „Dimensionality Reduction“. In Multilabel Classification, 115–31. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41111-8_7.

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Herrera, Francisco, Francisco Charte, Antonio J. Rivera und María J. del Jesus. „Imbalance in Multilabel Datasets“. In Multilabel Classification, 133–51. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41111-8_8.

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Herrera, Francisco, Francisco Charte, Antonio J. Rivera und María J. del Jesus. „Multilabel Software“. In Multilabel Classification, 153–91. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41111-8_9.

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Abe, Shigeo. „Introduction“. In Pattern Classification, 3–20. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0285-4_1.

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Konferenzberichte zum Thema "Classification":

1

Besse, P., P. Boisson und J. McGregor. „What Classification Rules For The Future And What Future For Classification?“ In Developments in Classification & International Regulation 2007. RINA, 2007. http://dx.doi.org/10.3940/rina.dcir.2007.15.

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Bień, Jan, und Małgorzata Gładysz-Bień. „Multi-level Classification of Bridge Defects in Asset Management“. In IABSE Symposium, Guimarães 2019: Towards a Resilient Built Environment Risk and Asset Management. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2019. http://dx.doi.org/10.2749/guimaraes.2019.1100.

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Annotation:

<p>In the paper a proposal of the unified multi-level classification of bridge defects declining condition of bridges is presented as one of the most important elements of asset management. General scheme of bridge degradation process is described with classifications of basic mechanisms of bridge degradation as well as stimulators of degradation mechanisms. Criteria of defects classification are proposed taking into account effects of activities of bridge degradation mechanisms. The proposed general conception of classification methodology of bridge defects is addressed to all types of bridge structures and all types of structural materials. Examples of hierarchical three-level classifications of defects are presented for concrete, steel and masonry bridge structures.</p>

3

Fadaie, Gholamreza. „The Influence of Classification on World View and Epistemology“. In InSITE 2008: Informing Science + IT Education Conference. Informing Science Institute, 2008. http://dx.doi.org/10.28945/3279.

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Worldview as a kind of man's look towards the world of reality has a severe influence on his classification of knowledge. In other words one may see in classification of knowledge the unity as well as plurality. This article deals with the fact that how classification takes place in man's epistemological process. Perception and epistemology are mentioned as the key points here. Philosophers are usually classifiers and their point of views forms the way they classify things and concepts. Relationship and how one looks at it in shaping the classification scheme is critical. The classifications which have been introduced up to now have had several models. They represent the kind of looking at, or point of view of their founders to the world. Aristotle, as a philosopher as well as an encyclopedist, is one of the great founders of knowledge classification. Afterwards the Islamic scholars followed him while some few rejected his model and made some new ones. If we divide all classifications according to their roots we may define them as human based classification, theology based classification, knowledge based classification, materialistic based classification such as Britannica's classification, and fact based classification. Tow broad approaches have been defined in this article: static and dynamic. The static approach refers to the traditional approaches and the dynamic one refers to the eight way of looking toward objects in order to realize them. The structure of classification has had its influence on epistemology, too. If the first cut on knowledge tree is fully defined, the branches would usually be consistent with it.

4

Khan, Mysha, und Pushpa Bhat. „Higgs event classification using Machine Learning“. In Higgs event classification using Machine Learning. US DOE, 2023. http://dx.doi.org/10.2172/1997111.

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Bozhchenko, Alexandr, und Sergey Semenov. „On the classification of damaging factors in forensic medicine“. In Issues of determining the severity of harm caused to human health as a result of the impact of a biological factor. ru: Publishing Center RIOR, 2020. http://dx.doi.org/10.29039/conferencearticle_5fdcb03a403b58.93332884.

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The article considers the disadvantages of modern forensic classifications of damaging factors. Attention is drawn to the fact that the mental damaging factor undoubtedly exists, but the assessment of its specific role in the formation of “damage” is the subject of forensic psychiatry, which is an independent medical discipline. The social factor mainly affects the behavior of an individual (population group), but its specific features are also not evaluated by methods and techniques of forensic medicine. There is a discrepancy between general and particular classifications — in particular, the forensic classification of explosions includes chemical, physical and nuclear explosions, with the latter's place in the composition of physical explosions. There is a violation of the continuity of classification — a typical error is a violation of hierarchy (the location in the same row of bacterial, viral, and antigenic or toxin damaging actions). It is concluded that due to the variability of the properties of damaging factors, we should not be talking about the classification of damaging factors (material bodies or phenomena), but about the classification of damaging properties of material bodies and phenomena.

6

Bruhns, H. „The New Imo Regulation For The Protection Of Fuel Tanks Affects Ship Designs“. In Developments in Classification & International Regulation 2007. RINA, 2007. http://dx.doi.org/10.3940/rina.dcir.2007.12.

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7

Motok, M. D., und J. Jovovic. „Wave Induced Shear Force And Bending Moment For Series Of Ships - Comparison & Some Interpolation Procedures“. In Developments in Classification & International Regulation 2007. RINA, 2007. http://dx.doi.org/10.3940/rina.dcir.2007.14.

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Jankowski, J., und M. Bogdaniuk. „Risk Model Used To Develop Goal-Based Standards For Ship Structures Of Single Side Bulk Carrier“. In Developments in Classification & International Regulation 2007. RINA, 2007. http://dx.doi.org/10.3940/rina.dcir.2007.09.

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Rizzo, C. M., und E. Rizzuto. „A Comparison Of Common Structural Rules With Previous Class Rules“. In Developments in Classification & International Regulation 2007. RINA, 2007. http://dx.doi.org/10.3940/rina.dcir.2007.01.

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Cazzulo, R., und A. Alderson. „Performance Standards Of Coatings In Ballast Tanks - Where A Class Society Could Help“. In Developments in Classification & International Regulation 2007. RINA, 2007. http://dx.doi.org/10.3940/rina.dcir.2007.06.

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Berichte der Organisationen zum Thema "Classification":

1

Robinson, David Gerald. Tissue Classification. Office of Scientific and Technical Information (OSTI), Januar 2015. http://dx.doi.org/10.2172/1177377.

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2

SHpinev, YU S. Investment classification. Институт государства и права РАН, 2020. http://dx.doi.org/10.18411/1311-1972-2020-00011.

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3

Li, C., O. Havel, A. Olariu, P. Martinez-Julia, J. Nobre und D. Lopez. Intent Classification. RFC Editor, Oktober 2022. http://dx.doi.org/10.17487/rfc9316.

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Aiken, Catherine. Classifying AI Systems. Center for Security and Emerging Technology, November 2021. http://dx.doi.org/10.51593/20200025.

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Annotation:

This brief explores the development and testing of artificial intelligence system classification frameworks intended to distill AI systems into concise, comparable and policy-relevant dimensions. Comparing more than 1,800 system classifications, it points to several factors that increase the utility of a framework for human classification of AI systems and enable AI system management, risk assessment and governance.

5

Hersey, Anne, Hrsg. ChEMBL Assay Classification. EMBL-EBI, Juni 2018. http://dx.doi.org/10.6019/chembl.assayclassification.

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Schau, M. Classification of granulites. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/128123.

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Brereton, S. J. Hazard classification methodology. Office of Scientific and Technical Information (OSTI), Juli 1996. http://dx.doi.org/10.2172/273808.

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DEPARTMENT OF THE ARMY WASHINGTON DC. Classification Management Tutorial. Fort Belvoir, VA: Defense Technical Information Center, Oktober 2006. http://dx.doi.org/10.21236/ada458946.

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Bogdanovic, D., B. Claise und C. Moberg. YANG Module Classification. RFC Editor, Juli 2017. http://dx.doi.org/10.17487/rfc8199.

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Marrs, Frank. Multiclass classification experiments. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1669069.

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