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Автор: Grafiati
Опубліковано: 11 вересня 2023

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Статті в журналах з теми "CLASSIFICATION OF RESEARCH"

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Kalandarova, Dilafruz Abdujamilovna. "RESEARCH CLASSIFICATION OF KARL REICHL." CURRENT RESEARCH JOURNAL OF PHILOLOGICAL SCIENCES 02, no. 11 (November 1, 2021): 29–33. http://dx.doi.org/10.37547/philological-crjps-02-11-08.

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The article discusses the fact that the famous German scientist Karl Reichl is a modern researcher, a classification of research created in the framework of his work in the process of recording and studying scientific, scientific-pedagogical, folklore and ethnographic materials.
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DeMers, Michael N. "Land Classification Research." International Journal of Applied Geospatial Research 5, no. 3 (July 2014): 82–92. http://dx.doi.org/10.4018/ijagr.2014070106.

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Land classification is so central to geography that its use, and the use of its derivative and corresponding products, is seldom even questioned. Since its earliest implementations land classification has adapted to changes in geographic scale and in the nature of the categorical systematics upon which it is based. Land classification has changed in its techniques and in how it adapts to technological changes, particularly those related to remote sensing and geographic information systems. The adaptation of land classification to digital pixel-based classification spawned a wide range of land classification error analysis techniques. These techniques do not easily transfer to non-pixel based classification error analysis as recent research on rapid land assessment methodologies and land change error analysis has shown. This disparity suggests a need to reevaluate the very nature of land classification research. To begin such an evaluation, this lecture provides a retrospective on the roots of land classification research, examines some of the milestones of that research, and describes the divergent paths such research has taken. It examines the importance of land classification in these times of ever decreasing global resources, and reviews its potential legal, social, and economic implications. Based on this retrospective, this paper advances the need for geographic researchers to envision land classification not only as a set of techniques, but more generally to focus on systematic geography in all its facets as a research agenda in its own right.
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3

Gracheva, R. G. "Genetic and substantive soil classifications and their applicability in geobotanical research." Plant Biology and Horticulture: theory, innovation, no. 149 (December 10, 2019): 55–64. http://dx.doi.org/10.36305/0201-7997-2019-149-55-64.

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The first soil classifications dividing soils according to their individual properties and reflecting the resource value of soils were, in fact, substantive classifications. The appearance of soil science that studies soils as special natural bodies changing in space according to natural laws has led to the emergence of genetic soil classifications. The most detailed multilevel Classification of soils of the USSR (1977) is ecological genetic classification based on the relationship between soils and soil formation conditions, where the type of vegetation cover is the top hierarchy level. With the development of soil science, inconsistencies between the genetic classification and the growing volume of knowledge, difficulties in its formalization, and contradictions with the zonal approach have appeared. A paradigm of substantive classification has emerged based on modern measurable morphological and analytical indicators of soils. It is most fully implemented in the World reference base for soil resources (WRB); a compromise option is the substantive- genetic Soil Classification of Russia (2004). In these classifications, for the first time soils with strong human influence (Antrosols and Technosols) are included at a high classification level. The tasks of substantive classifications are the certainty of soil identification; however, discrete classification cells with tight boundaries do not correspond to the continual nature of soils. When using WRB 2014, geobotanical studies receive a certain “freedom” from soils, at the same time, the rigid framework of the parameters of soil properties makes it possible to more accurately identify soil-depended condition of the vegetation cover.
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4

Ilic, Marina. "Sociometric research in pedagogy." Zbornik Instituta za pedagoska istrazivanja 45, no. 1 (2013): 24–41. http://dx.doi.org/10.2298/zipi1301024i.

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The use of sociometric research in pedagogy implies consideration of methodological demands pertaining to the selection and formulation of sociometric criteria, the selection of sociometric techniques, instruments and sociometric group classification methods. Various authors agree that sociometric criteria are basic relevant activities of the group they refer to and that they should be meaningful, understandable to every group member and clearly and precisely formulated. The selection of the appropriate sociometric technique and instrument has to be adjusted with the goal of sociometric research, along with the consideration of the advantages and short?comings of different sociometric techniques and instruments. The existing research provides very divergent results on the adequacy of different classification methods in identifying stable sociometric groups. Still, the majority of studies have confirmed that the two-dimensional rating scale method yields more stable classifications than the classification methods based on peer nominations, as well as that it is methodologically more justifiable to use cluster analysis in identifying stable sociometric status groups than the traditional classification methods.
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Vizine-Goetz, Diane. "Classification Research at OCLC." Journal of Library Administration 34, no. 1-2 (December 2001): 199–208. http://dx.doi.org/10.1300/j111v34n01_30.

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6

Berenbaum, Howard. "Classification and psychopathology research." Journal of Abnormal Psychology 122, no. 3 (2013): 894–901. http://dx.doi.org/10.1037/a0033096.

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Weihs, Claus, Uwe Ligges, Fabian Mörchen, and Daniel Müllensiefen. "Classification in music research." Advances in Data Analysis and Classification 1, no. 3 (November 21, 2007): 255–91. http://dx.doi.org/10.1007/s11634-007-0016-x.

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Feinstein, Alvan R. "Classification of epidemiologic research." Journal of Clinical Epidemiology 41, no. 8 (January 1988): 805. http://dx.doi.org/10.1016/0895-4356(88)90168-0.

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Abramson, J. "Classification of epidemiologic research." Journal of Clinical Epidemiology 42, no. 9 (1989): 819–20. http://dx.doi.org/10.1016/0895-4356(89)90091-7.

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Olesen, Jes. "The Future of Headache Classification and Classification Research." Cephalalgia 29, no. 12 (December 2009): 1240–41. http://dx.doi.org/10.1111/j.1468-2982.2009.01936.x.

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Дисертації з теми "CLASSIFICATION OF RESEARCH"

1

Francis, Paul John. "The classification of quasar spectra." Thesis, University of Cambridge, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239185.

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2

Wei, Zhihua. "The research on chinese text multi-label classification." Thesis, Lyon 2, 2010. http://www.theses.fr/2010LYO20025/document.

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Text Classification (TC) which is an important field in information technology has many valuable applications. When facing the sea of information resources, the objects of TC are more complicated and diversity. The researches in pursuit of effective and practical TC technology are fairly challenging. More and more researchers regard that multi-label TC is more suited for many applications. This thesis analyses the difficulties and problems in multi-label TC and Chinese text representation based on a mass of algorithms for single-label TC and multi-label TC. Aiming at high dimensionality in feature space, sparse distribution in text representation and poor performance of multi-label classifier, this thesis will bring forward corresponding algorithms from different angles.Focusing on the problem of dimensionality “disaster” when Chinese texts are represented by using n-grams, two-step feature selection algorithm is constructed. The method combines filtering rare features within class and selecting discriminative features across classes. Moreover, the proper value of “n”, the strategy of feature weight and the correlation among features are discussed based on variety of experiments. Some useful conclusions are contributed to the research of n-gram representation in Chinese texts.In a view of the disadvantage in Latent Dirichlet Allocation (LDA) model, that is, arbitrarily revising the variable in smooth process, a new strategy for smoothing based on Tolerance Rough Set (TRS) is put forward. It constructs tolerant class in global vocabulary database firstly and then assigns value for out-of-vocabulary (oov) word in each class according to tolerant class.In order to improve performance of multi-label classifier and degrade computing complexity, a new TC method based on LDA model is applied for Chinese text representation. It extracts topics statistically from texts and then texts are represented by using the topic vector. It shows competitive performance both in English and in Chinese corpus.To enhance the performance of classifiers in multi-label TC, a compound classification framework is raised. It partitions the text space by computing the upper approximation and lower approximation. This algorithm decomposes a multi-label TC problem into several single-label TCs and several multi-label TCs which have less labels than original problem. That is, an unknown text should be classified by single-label classifier when it is partitioned into lower approximation space of some class. Otherwise, it should be classified by corresponding multi-label classifier.An application system TJ-MLWC (Tongji Multi-label Web Classifier) was designed. It could call the result from Search Engines directly and classify these results real-time using improved Naïve Bayes classifier. This makes the browse process more conveniently for users. Users could locate the texts interested immediately according to the class information given by TJ-MLWC
La thèse est centrée sur la Classification de texte, domaine en pleine expansion, avec de nombreuses applications actuelles et potentielles. Les apports principaux de la thèse portent sur deux points : Les spécificités du codage et du traitement automatique de la langue chinoise : mots pouvant être composés de un, deux ou trois caractères ; absence de séparation typographique entre les mots ; grand nombre d’ordres possibles entre les mots d’une phrase ; tout ceci aboutissant à des problèmes difficiles d’ambiguïté. La solution du codage en «n-grams »(suite de n=1, ou 2 ou 3 caractères) est particulièrement adaptée à la langue chinoise, car elle est rapide et ne nécessite pas les étapes préalables de reconnaissance des mots à l’aide d’un dictionnaire, ni leur séparation. La classification multi-labels, c'est-à-dire quand chaque individus peut être affecté à une ou plusieurs classes. Dans le cas des textes, on cherche des classes qui correspondent à des thèmes (topics) ; un même texte pouvant être rattaché à un ou plusieurs thème. Cette approche multilabel est plus générale : un même patient peut être atteint de plusieurs pathologies ; une même entreprise peut être active dans plusieurs secteurs industriels ou de services. La thèse analyse ces problèmes et tente de leur apporter des solutions, d’abord pour les classifieurs unilabels, puis multi-labels. Parmi les difficultés, la définition des variables caractérisant les textes, leur grand nombre, le traitement des tableaux creux (beaucoup de zéros dans la matrice croisant les textes et les descripteurs), et les performances relativement mauvaises des classifieurs multi-classes habituels
文本分类是信息科学中一个重要而且富有实际应用价值的研究领域。随着文本分类处理内容日趋复杂化和多元化，分类目标也逐渐多样化，研究有效的、切合实际应用需求的文本分类技术成为一个很有挑战性的任务，对多标签分类的研究应运而生。本文在对大量的单标签和多标签文本分类算法进行分析和研究的基础上，针对文本表示中特征高维问题、数据稀疏问题和多标签分类中分类复杂度高而精度低的问题，从不同的角度尝试运用粗糙集理论加以解决，提出了相应的算法，主要包括：针对n-gram作为中文文本特征时带来的维数灾难问题，提出了两步特征选择的方法，即去除类内稀有特征和类间特征选择相结合的方法，并就n-gram作为特征时的n值选取、特征权重的选择和特征相关性等问题在大规模中文语料库上进行了大量的实验，得出一些有用的结论。针对文本分类中运用高维特征表示文本带来的分类效率低，开销大等问题，提出了基于LDA模型的多标签文本分类算法，利用LDA模型提取的主题作为文本特征，构建高效的分类器。在PT3多标签分类转换方法下，该分类算法在中英文数据集上都表现出很好的效果，与目前公认最好的多标签分类方法效果相当。针对LDA模型现有平滑策略的随意性和武断性的缺点，提出了基于容差粗糙集的LDA语言模型平滑策略。该平滑策略首先在全局词表上构造词的容差类，再根据容差类中词的频率为每类文档的未登录词赋予平滑值。在中英文、平衡和不平衡语料库上的大量实验都表明该平滑方法显著提高了LDA模型的分类性能，在不平衡语料库上的提高尤其明显。针对多标签分类中分类复杂度高而精度低的问题，提出了一种基于可变精度粗糙集的复合多标签文本分类框架，该框架通过可变精度粗糙集方法划分文本特征空间，进而将多标签分类问题分解为若干个两类单标签分类问题和若干个标签数减少了的多标签分类问题。即，当一篇未知文本被划分到某一类文本的下近似区域时，可以直接用简单的单标签文本分类器判断其类别；当未知文本被划分在边界域时，则采用相应区域的多标签分类器进行分类。实验表明，这种分类框架下，分类的精确度和算法效率都有较大的提高。本文还设计和实现了一个基于多标签分类的网页搜索结果可视化系统（MLWC），该系统能够直接调用搜索引擎返回的搜索结果，并采用改进的Naïve Bayes多标签分类算法实现实时的搜索结果分类，使用户可以快速地定位搜索结果中感兴趣的文本。
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Mayo, Robert William. "An evaluation of social grade as a classification scheme." Thesis, City University London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.312902.

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4

Dunlap, James 1963. "Classification and analysis of longwall delays." Thesis, Virginia Tech, 1990. http://hdl.handle.net/10919/42403.

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Egan, Shaun Peter. "A framework for high speed lexical classification of malicious URLs." Thesis, Rhodes University, 2014. http://hdl.handle.net/10962/d1011933.

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Phishing attacks employ social engineering to target end-users, with the goal of stealing identifying or sensitive information. This information is used in activities such as identity theft or financial fraud. During a phishing campaign, attackers distribute URLs which; along with false information, point to fraudulent resources in an attempt to deceive users into requesting the resource. These URLs are made obscure through the use of several techniques which make automated detection difficult. Current methods used to detect malicious URLs face multiple problems which attackers use to their advantage. These problems include: the time required to react to new attacks; shifts in trends in URL obfuscation and usability problems caused by the latency incurred by the lookups required by these approaches. A new method of identifying malicious URLs using Artificial Neural Networks (ANNs) has been shown to be effective by several authors. The simple method of classification performed by ANNs result in very high classification speeds with little impact on usability. Samples used for the training, validation and testing of these ANNs are gathered from Phishtank and Open Directory. Words selected from the different sections of the samples are used to create a `Bag-of-Words (BOW)' which is used as a binary input vector indicating the presence of a word for a given sample. Twenty additional features which measure lexical attributes of the sample are used to increase classification accuracy. A framework that is capable of generating these classifiers in an automated fashion is implemented. These classifiers are automatically stored on a remote update distribution service which has been built to supply updates to classifier implementations. An example browser plugin is created and uses ANNs provided by this service. It is both capable of classifying URLs requested by a user in real time and is able to block these requests. The framework is tested in terms of training time and classification accuracy. Classification speed and the effectiveness of compression algorithms on the data required to distribute updates is tested. It is concluded that it is possible to generate these ANNs in a frequent fashion, and in a method that is small enough to distribute easily. It is also shown that classifications are made at high-speed with high-accuracy, resulting in little impact on usability.
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Qi, Wang. "Studies in the Dynamics of Science : Exploring emergence, classification, and interdisciplinarity." Doctoral thesis, KTH, Industriell ekonomi och organisation (Inst.), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-184724.

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The dynamic nature of science is embodied in the growth of knowledge in magnitude and the transformation of knowledge in structure. More specifically, the growth in magnitude is indicated by a sharp increase in the number of scientific publications in recent decades. The transformation of knowledge occurs as the boundaries of scientific disciplines become increasingly less distinct, resulting in a complicated situation wherein disciplines and interdisciplinary research topics coexist and co-evolve. Knowledge production in such a context creates challenges for the measurement of science.This thesisaims to develop more flexible bibliometric methodologies in order to address some of the challenges to measuring science effectively. To be specific, this thesis1) proposes a new approach for identifying emerging research topics; 2) measuresthe interdisciplinarity of research topics; 3) explores the accuracy of the journal classification systems of the Web of Science and Scopus; 4) examines the role of cognitive distance in grant decisions; and 5) investigates the effect of cognitive distance between collaborators on their research output. The data used in this thesisaremainly from the in-house Web of Science and Scopus databases of the Centre for Science and Technology Studies (CWTS) at Leiden University. Quantitativeanalyses, in particular bibliometric analyses,are the main research methodologies employed in this thesis. Furthermore, this thesis primarily offers methodological contributions, proposing a series of approaches designed to tackle the challenges created by the dynamics of science. While the major contribution of this dissertation lies in the improvement of certain bibliometric approaches, it also enhances the understanding of the current system of science. In particular, the approaches and research findings presented here have implications for various stakeholders, including publishing organizations, bibliographic database producers, research policy makers, and research funding agencies. Indeed, these approaches could be built into a software tool and thereby be made available to researchers beyond the field of bibliometric studies.

QC 20160406

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Vaidya, Priyanka S. "Artificial Intelligence Approach to Breast Cancer Classification." University of Akron / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=akron1240957599.

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Monaghan, Mark Peter. "The turmoil of evidence : research utilisation in UK drug classification." Thesis, University of Leeds, 2008. http://etheses.whiterose.ac.uk/701/.

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This thesis investigates the thorny relationship between evidence utilisation and policy making in a heavily politicised policy area. Expectations for the conflux of researcha nd policy formulation have been consolidated in the last decade under the banner of 'evidence-based policy'. In recent times, the debateo ver the nature and utility of evidence-based policy has become much more sophisticated. No longer can the connection between evidence utilisation and policy formulation be conceived in terms of evidence shaping policy outcomes or, conversely, policy being evidence free, where evidence has no impact. Such conceptualisations persist, however, in heavily politicised policy areas, where there is intense media scrutiny of decision making, a lack of consensus on its direction, prolonged conflict between competing interest and stakeholder groups and a permeating sense of crisis. These tend to relate to more 'macro' policy areas, not usually the remit of evidence-based policy-making and evaluative research. Using recent and ongoing developments in UK drug classification policy as a case-study, an explanatory framework of the complex role and nature of evidence in heavily politicised policy areas is developed. Central to this, is the use of a methodological approach that can account for the role of conflict in the policy process. A modified version of the Advocacy Coalition Framework is employed to this end. This, in turn, allows for a range of data-collection methods to be used, including observation and documentary analysis of Parliamentary Select Committee hearings alongside qualitative interviews with a wide-range of key policy actors involved in the decision-making process. From this a nuanced account of the evidence and policy relationship in such contexts is ascertained,which departs from the more established models explaining the evidence and policy nexus. Traditionally, such explanations have been conceived as models of research utilisation. In this research it is suggested that these do not translate effectively as models of evidence-based policy-making. This is because they are beset with some, or all, of the following problems: a) they focus more on 'research' rather than the broader concept of 'evidence'; b) they operate with a static view of the policy process where there is a direct connection between research and policy; c) they restrict the role of evidence to one of policy outcomes, rather than viewing the role of evidence in the process of decision-making; d) they assume that research is the defining influence on the decision-making processe; e) they operate at a high level of abstraction, offering little account of how research is selected for use in decision making. Consequently, a newer addition to the literature is developed, which, it is claimed, avoids these shortcomings.
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Dutta, Bidyarthi, Krishnapada Majumder, and B. K. Sen. "Classification of Keywords Extracted from Research Articles Published in Science Journals." National Institute of Science Communication and Information Resources, 2008. http://hdl.handle.net/10150/105938.

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This paper is based on an analytical study of 335 keywords extracted from titles and abstracts of 70 research articles, taking ten from each year starting from 2000 to 2006, in decreasing order of relevance, on the subject Fermi Liquid, which is a specific subject under the broad area of Condensed Matter Physics. The research articles have been collected from the bibliographic database of INSPEC. The keywords are indexed to critically examine its physical structure that is composed of three fundamental kernels, viz. keyphrase, modulator and qualifier. The keyphrase reflects the central concept, which is usually post-coordinated by the modulator to amend the central concept in accordance with the relevant context. The qualifier comes after the modulator to describe the particular state of the central concept and/or amended concept. The keywords are further classified in 16 classes on the basis of the four parameters, viz. Associativeness, chronological appearance, frequency of occurrence and category. The taxonomy of keywords will enable to analyze research-trend of a subject and to identify potential research-areas of a subject.
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10

Hajdu, Barat Agnes. "Multilevel education, training, traditions and research on UDC in Hungary." UDC Consortium, 2007. http://hdl.handle.net/10150/105607.

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This paper explores the theory and practice of education in schools and further education as two levels of the Information Society in Hungary. LIS education is considered the third level over previous ones. The curriculum and content of different subjects in schools and their relationship to libraries is summarized, as well as the training programmes for librarians, especially concerning knowledge organization. The long history of UDC usage in Hungary is surveyed, highlighting principal milestones and people. The paper provides a brief overview of recent developments, the situation after the new Hungarian edition, and current UDC usage and research directions.
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Книги з теми "CLASSIFICATION OF RESEARCH"

1

D, Hensley Oliver, and SRA Research Symposium (1984 : San Diego, Calif.), eds. The Classification of research. Lubbock, Tex: Texas Tech University Press, 1988.

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2

ASIS SIG/CR Classification Research Workshop (1st 1990 Toronto, Ont.). Advances in classification research: Proceedings of the 1st ASIS SIG/CR Classification Research Workshop. Edited by Humphrey Susanne M, Kwasnik Barbara H, American Society for Information Science. Special Interest Group/Classification Research., and American Society for Information Science. Meeting. Medford, N.J: Published by Learned Information, Inc. for the American Society for Information Science, 1991.

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3

Locarek-Junge, Hermann, and Claus Weihs, eds. Classification as a Tool for Research. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-10745-0.

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4

Council, Universities Funding, ed. Classification of sources of research income. [London]: [Universities Funding Council], 1989.

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5

ASIS, SIG/CR Classification Research Workshop (13th 2002 Philadelphis PA). Advances in classification research, volume 13: Proceedings of the 13th ASIS SIG/CR Classification Research Workshop. Medford, N.J: Published by Information Today, Inc. for the American Society for Information Science and Technology, 2004.

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6

1945-, Fidel Raya, American Society for Information Science. Special Interest Group/Classification Research., and American Society for Information Science. Meeting, eds. Advances in classification research, volume 5: Proceedings of the 5th ASIS SIG/CR Classification Research Workshop. Medford, N.J: Published by Information Today, Inc. for the American Society for Information Science, 1996.

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7

ASIS SIG/CR Classification Research Workshop (7th 1996 Baltimore, Maryland). Advances in classification research, volume 7: Proceedings of the 7th ASIS SIG/CR Classification Research Workshop. Edited by Solomon Paul and American Society for Information Science. Special Interest Group/Classification Research. Medford, N.J: Published by Information Today, Inc. for the American Society for Information Science, 1997.

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8

ASIS SIG/CR Classification Research Workshop (8th 1997 Washington, DC). Advances in classification research, volume 8: Proceedings of the 8th ASIS SIG/CR Classification Research Workshop. Edited by Efthimiadis Efthimis Nikolaos and American Society for Information Science. Special Interest Group/Classification Research. Medford, N.J: Published by Information Today, Inc. for the American Society for Information Science, 1998.

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9

ASIS SIG/CR Classification Research Workshop (4th 1993 Columbus, Ohio). Advances in classification research, volume 4: Proceedings of the 4th ASIS SIG/CR Classification Research Workshop. Edited by Smith Philip J. 1951-, American Society for Information Science. Special Interest Group/Classification Research., and American Society for Information Science. Meeting. Medford, N.J: Published by Information Today, Inc. for the American Society for Information Science, 1995.

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10

ASIS SIG/CR Classification Research Workshop (6th 1995 Chicago, Ill.). Advances in classification research, volume 6: Proceedings of the 6th ASIS SIG/CR Classification Research Workshop. Edited by Schwartz Raymond P, Beghtol Clare 1942-, American Society for Information Science. Special Interest Group/Classification Research., and American Society for Information Science. Meeting. Medford, N.J: Published by Information Today, Inc. for the American Society for Information Science, 1998.

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Частини книг з теми "CLASSIFICATION OF RESEARCH"

1

Chien, Yuehmei, Ning Yan, and Chingwei D. Shin. "Mastery Classification of Diagnostic Classification Models." In Quantitative Psychology Research, 251–62. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19977-1_18.

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Luccioni, H. "Research Strategies and Tactics in Psychiatric Research: Introduction." In Clinical Psychopathology Nomenclature and Classification, 283–84. Boston, MA: Springer US, 1985. http://dx.doi.org/10.1007/978-1-4899-5049-9_48.

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3

Breebaart, Jeroen, and Martin F. McKinney. "Features for Audio Classification." In Philips Research, 113–29. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-94-017-0703-9_6.

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Ghiya, Anish Sudhir, Vaibhav Vijay, Aditi Ranganath, Prateek Chaturvedi, Sharmila Banu Kather, and Balakrushna Tripathy. "Weather Classification." In Research Advances in Intelligent Computing, 211–24. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003320340-17.

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5

Widiger, Thomas A., and Stephanie Mullins-Sweatt. "Classification and diagnosis research." In APA handbook of clinical psychology: Theory and research (Vol. 2)., 377–91. Washington: American Psychological Association, 2016. http://dx.doi.org/10.1037/14773-015.

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6

Ulmer, Marlin Wolf. "Literature Classification." In Operations Research/Computer Science Interfaces Series, 103–13. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55511-9_7.

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7

Axelson, Marta L., and David Brinberg. "Food Classification Systems." In Recent Research in Psychology, 135–72. New York, NY: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4613-9661-1_7.

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8

Schwaiger, M. "Two-Mode Classification in Advertising Research." In Classification and Knowledge Organization, 596–603. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-59051-1_63.

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9

Scherer, Rafał. "Concluding Remarks and Challenges for Future Research." In Multiple Fuzzy Classification Systems, 129–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30604-4_9.

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10

Olson, David L., and Özgür M. Araz. "Classification Models." In International Series in Operations Research & Management Science, 87–104. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-28113-6_7.

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Тези доповідей конференцій з теми "CLASSIFICATION OF RESEARCH"

1

Borne, K., J. Becla, I. Davidson, A. Szalay, J. A. Tyson, and Coryn A. L. Bailer-Jones. "The LSST Data Mining Research Agenda." In CLASSIFICATION AND DISCOVERY IN LARGE ASTRONOMICAL SURVEYS: Proceedings of the International Conference: “Classification and Discovery in Large Astronomical Surveys”. AIP, 2008. http://dx.doi.org/10.1063/1.3059074.

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2

Xu, Zhijuan, Lizhen Liu, Wei Song, and Chao Du. "Text genre classification research." In 2017 International Conference on Computer, Information and Telecommunication Systems (CITS). IEEE, 2017. http://dx.doi.org/10.1109/cits.2017.8035329.

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3

Pan, Yuzhu. "Research on Leaf Classification under Different Classification Methods." In 2021 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS). IEEE, 2021. http://dx.doi.org/10.1109/icpics52425.2021.9524160.

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4

Joshua, Liju, and Koshy Varghese. "Construction Activity Classification Using Accelerometers." In Construction Research Congress 2010. Reston, VA: American Society of Civil Engineers, 2010. http://dx.doi.org/10.1061/41109(373)7.

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5

Yan-Feng Fan, De-Xian Zhang, and Hua-Can He. "A new classification algorithm research." In International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR '07. IEEE, 2007. http://dx.doi.org/10.1109/icwapr.2007.4420744.

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6

Gao, Junting, Liping Zhang, Fengrong Zhao, and Ye Zhai. "Research on Software Defect Classification." In 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2019. http://dx.doi.org/10.1109/itnec.2019.8729440.

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7

Abdelhamid, Neda, Ahmad Abdul Jabbar, and Fadi Thabtah. "Associative Classification Common Research Challenges." In 2016 45th International Conference on Parallel Processing Workshops (ICPPW). IEEE, 2016. http://dx.doi.org/10.1109/icppw.2016.67.

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8

Liao, YuanLai. "Research on Image Region Classification." In Advanced Science and Technology 2016. Science & Engineering Research Support soCiety, 2016. http://dx.doi.org/10.14257/astl.2016.121.70.

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9

Guandong, Shen. "Research on Network Image Classification." In 2015 Sixth International Conference on Intelligent Systems Design and Engineering Applications (ISDEA). IEEE, 2015. http://dx.doi.org/10.1109/isdea.2015.166.

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Chen, Tianhua, Elans Grabs, Aleksandrs Ipatovs, Ernests Pētersons, and Arnis Ancāns. "Bitrate-based Video Traffic Classification." In 2023 Photonics & Electromagnetics Research Symposium (PIERS). IEEE, 2023. http://dx.doi.org/10.1109/piers59004.2023.10221281.

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Звіти організацій з теми "CLASSIFICATION OF RESEARCH"

1

Howden, Stephan, and Denis Wiesenburg. Hydrographic Research for Marine Vertical Positioning and Seabed Classification. Fort Belvoir, VA: Defense Technical Information Center, September 2003. http://dx.doi.org/10.21236/ada630087.

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2

Russell, Terera L., Douglas H. Reynolds, and John P. Campbell. Building a Joint-Service Classification Research Roadmap: Individual Differences Measurement. Fort Belvoir, VA: Defense Technical Information Center, April 1994. http://dx.doi.org/10.21236/ada280155.

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3

Knapp, Deirdre J., and John P. Campbell. Building a Joint-Service Classification Research Roadmap: Criterion- Related Issues. Fort Belvoir, VA: Defense Technical Information Center, July 1993. http://dx.doi.org/10.21236/ada269735.

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4

Blanding, Howard C. Training Initiatives for Classification Society Personnel (The National Shipbuilding Research Program). Fort Belvoir, VA: Defense Technical Information Center, September 1992. http://dx.doi.org/10.21236/ada458091.

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5

Kavanagh, Michael J., Walter C. Borman, Jerry W. Hedge, and R. B. Gould. Job Performance Measurement Classification Scheme for Validation Research in the Military. Fort Belvoir, VA: Defense Technical Information Center, February 1986. http://dx.doi.org/10.21236/ada164837.

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6

Maddux, Gary A. Manufacturing Research of Technologies for Detection, Discrimination and Classification of Targets in Clutter. Fort Belvoir, VA: Defense Technical Information Center, August 1999. http://dx.doi.org/10.21236/ada368502.

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7

Sadacca, Robert. The Army Family Research Program and the Selection and Classification Project Data Base. Fort Belvoir, VA: Defense Technical Information Center, July 1990. http://dx.doi.org/10.21236/ada227585.

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8

Patnaik, P., P. Amoroso, K. Mundt, and C. Bigelow. Disabling Knee Injury in the United States Army: Classification of Injury for Etiologic Research. Fort Belvoir, VA: Defense Technical Information Center, October 2000. http://dx.doi.org/10.21236/ada383861.

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9

Orlansky, Jesse, Frances Grafton, Clessen J. Martin, William Alley, and Bruce Bloxom. The Current Status of Research and Development on Selection and Classification of Enlisted Personnel. Fort Belvoir, VA: Defense Technical Information Center, June 1990. http://dx.doi.org/10.21236/ada227599.

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Kavanagh, Michael J., Walter C. Borman, Jerry W. Hedge, and R. B. Gould. Job Performance Measurement in the Military: A Classification Scheme, Literature Review, and Directions for Research. Fort Belvoir, VA: Defense Technical Information Center, September 1987. http://dx.doi.org/10.21236/ada185752.

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