Статті в журналах: "Category classification"

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&NA;. "Levemir granted pregnancy Category B classification." Reactions Weekly &NA;, no. 1397 (April 2012): 2. http://dx.doi.org/10.2165/00128415-201213970-00005.

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&NA;. "Pregnancy Category D classification for Myfortic." Inpharma Weekly &NA;, no. 1617 (December 2007): 17. http://dx.doi.org/10.2165/00128413-200716170-00051.

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Kazan, Serap, and Hakan Karakoca. "Product Category Classification with Machine Learning." Sakarya University Journal of Computer and Information Sciences 2, no. 1 (April 30, 2019): 18–27. http://dx.doi.org/10.35377/saucis.02.01.523139.

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4

Li, Luoqing, Chuanwu Yang, and Qiwei Xie. "1D embedding multi-category classification methods." International Journal of Wavelets, Multiresolution and Information Processing 14, no. 02 (March 2016): 1640006. http://dx.doi.org/10.1142/s0219691316400063.

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In this paper, we propose a novel semi-supervised multi-category classification method based on one-dimensional (1D) multi-embedding. Based on the multiple 1D embedding based interpolation technique, we embed the high-dimensional data into several different 1D manifolds and perform binary classification firstly. Then we construct the multi-category classifiers by means of one-versus-rest and one-versus-one strategies separately. A weight strategy is employed in our algorithm for improving the classification performance. The proposed method shows promising results in the classification of handwritten digits and facial images.

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Ross, Brian H., Susan A. Gelman, and Karl S. Rosengren. "Children's category-based inferences affect classification." British Journal of Developmental Psychology 23, no. 1 (March 2005): 1–24. http://dx.doi.org/10.1348/026151004x20108.

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Yamauchi, Takashi, and Arthur B. Markman. "Category Learning by Inference and Classification." Journal of Memory and Language 39, no. 1 (July 1998): 124–48. http://dx.doi.org/10.1006/jmla.1998.2566.

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Feroe, Aliya G., Rachel A. Flaugh, Tara A. Baxter, Aditi S. Majumdar, Patricia E. Miller, and Mininder S. Kocher. "Novel Mri Classification for Osteochondritis Dissecans of the Knee: A Validation Study." Orthopaedic Journal of Sports Medicine 10, no. 5_suppl2 (May 1, 2022): 2325967121S0041. http://dx.doi.org/10.1177/2325967121s00412.

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Background: A simpler three-category magnetic resonance imaging (MRI) classification system for osteochondritis dissecans (OCD) of the knee was recently shown to have comparable reliability to the existing five-category Hefti system. The validity and clinical utility of this simpler system as an alternative to the Hefti system has yet to be established. Hypothesis/Purpose: The purpose of this study was to assess whether a novel, simpler three-category MRI classification system for OCD of the knee demonstrates equal or better validity and correlates more strongly with treatment than the five-category Hefti classification. Methods: Demographic data and arthroscopic findings were collected from the medical and surgical records of 144 knees of children and adolescents with arthroscopically diagnosed knee OCD. MRI assessment of OCD lesions was conducted by two independent raters. Inter-rater reliability for novel and Hefti classifications was assessed by estimating weighted kappa ( kw). Agreement between MRI classification and arthroscopic findings was assessed by estimating kw coefficients. Classifications were dichotomized into 1,2 versus 3 for the novel system, and the sensitivity and specificity of classification was estimated as compared to arthroscopic findings. Correlation between arthroscopic classification and treatment type was assessed by estimating Spearman’s coefficient. Results: There was substantial interrater agreement with the proposed system’s ratings on MRI ( kw=0.66; 955 CI=0.56-0.75) and moderate agreement for Hefti system’s ratings on MRI ( kw=0.57; 955 CI=0.47-0.67) (Table 1). There was no difference detected in the agreement statistics for the proposed versus Hefti classifications (p=0.89). Binary agreement of the novel classification using dichotomous categories was slightly worse than the three-category classification. When 1&2s were combined, the agreement was moderate with k=0.41 (95% CI=0.25-0.58) and when 2&3s were combined, the agreement was fair with k=0.34 (95% CI=0.21-0.48). There was strong correlation between novel classification on arthroscopy with treatment type ( r=0.85; 95% CI = 0.80-0.89) and between Hefti classification and treatment type ( r=0.82; 95% CI = 0.75-0.86) (Table 2). Conclusion: The validity and clinical utility of the proposed three-category MRI classification system for knee OCD is comparable to that of the five-category Hefti system. This simpler classification system provides a foundation for the subsequent establishment of an operative treatment algorithm for knee OCD. [Table: see text][Table: see text]

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Ragab, Ahmed Refaat Sobhy Ahmed. "A New Classification for Ad-Hoc Network." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 14 (August 28, 2020): 214. http://dx.doi.org/10.3991/ijim.v14i14.14871.

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<span>This paper focus on developing a new practical classification for ad-hoc networks, were all the past classifications revolve upon three main categories respectively, mobile ad- hoc network (MANET), Vehicle ad-hoc network (VANET) and Flying ad-hoc network (FANET). My new classification will illustrate Underwater vehicle ad-hoc network (UWVANET) as the fourth category in ad-hoc main classification, showing the powerful and the weakness of each category defined.</span>

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Amini, Massoud, George A. Elliott, and Nasser Golestani. "The Category of Bratteli Diagrams." Canadian Journal of Mathematics 67, no. 5 (October 1, 2015): 990–1023. http://dx.doi.org/10.4153/cjm-2015-001-8.

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AbstractA category structure for Bratteli diagrams is proposed and a functor from the category of AF algebras to the category of Bratteli diagrams is constructed. Since isomorphism of Bratteli diagrams in this category coincides with Bratteli’s notion of equivalence, we obtain in particular a functorial formulation of Bratteli’s classification of AF algebras (and at the same time, of Glimm’s classification of UHF algebras). It is shown that the three approaches to classification of AF algebras, namely, through Bratteli diagrams, K-theory, and a certain natural abstract classifying category, are essentially the same from a categorical point of view.

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10

Zhong, Jincheng, and Shuhui Chen. "Efficient multi-category packet classification using TCAM." Computer Communications 169 (March 2021): 1–10. http://dx.doi.org/10.1016/j.comcom.2020.12.027.

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TANG, Yingjun, De XU, Guanghua GU, and Shuoyan LIU. "Category Constrained Learning Model for Scene Classification." IEICE Transactions on Information and Systems E92-D, no. 2 (2009): 357–60. http://dx.doi.org/10.1587/transinf.e92.d.357.

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Cohen, Andrew L., Robert M. Nosofsky, and Safa R. Zaki. "Category variability, exemplar similarity, and perceptual classification." Memory & Cognition 29, no. 8 (December 2001): 1165–75. http://dx.doi.org/10.3758/bf03206386.

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Levering, Kimery R., and Kenneth J. Kurtz. "Observation versus classification in supervised category learning." Memory & Cognition 43, no. 2 (September 5, 2014): 266–82. http://dx.doi.org/10.3758/s13421-014-0458-2.

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14

Qin, Lifeng. "Category Related BoW Model for Image Classification." Journal of Information and Computational Science 12, no. 9 (June 10, 2015): 3547–54. http://dx.doi.org/10.12733/jics20106109.

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Hadas, David, Nathan Intrator, and Galit Yovel. "Rapid Object Category Adaptation during Unlabelled Classification." Perception 39, no. 9 (January 2010): 1230–39. http://dx.doi.org/10.1068/p6658.

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16

Johansen, Mark K., and John K. Kruschke. "Category Representation for Classification and Feature Inference." Journal of Experimental Psychology: Learning, Memory, and Cognition 31, no. 6 (2005): 1433–58. http://dx.doi.org/10.1037/0278-7393.31.6.1433.

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17

Koehler, Derek J. "Probability judgment in three-category classification learning." Journal of Experimental Psychology: Learning, Memory, and Cognition 26, no. 1 (2000): 28–52. http://dx.doi.org/10.1037/0278-7393.26.1.28.

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18

Paek, E. G., J. R. Wullert II, and J. S. Patel. "Optical learning machine for multi-category classification." Optics News 15, no. 12 (December 1, 1989): 28. http://dx.doi.org/10.1364/on.15.12.000028.

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19

Jokić, Davor. "Field classification in Dimensions." Textile & leather review 2, no. 3 (September 9, 2019): 145–53. http://dx.doi.org/10.31881/tlr.2019.31.

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Given the latest research on Dimensions classification, this article discusses the novelty of such classification in the field of textile technology from the standpoint of Croatian scientific career advancement system. New machine learning article based classification system is compared to a traditional journal based classification system brought by the Web of Science and Scopus in terms of evaluation significance. The starting point of assigned category comparison were 13 journals indexed in the Web of Science in just one common category - Materials science, Textiles. Since Scopus does not have a unique category for the textile technology a list of 11 assigned categories was put in the comparison. Lastly, 58 research fields assigned to the articles published in mentioned journals indexed in Dimensions were analyzed for validity. Results show that the unique category of Textiles in Web of Science fully fits the field of textile technology from Croatian point of view. Scopus model with multi category assignment is not so reliable and useful in field evaluation. Lastly, Dimensions with its novel approach failed to validly classify indexed publications.

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Maiorova, K. V., and V. I. Serebryannikova. "A new conceptual approach to classification modifications of the transport category aircraft." Science, technologies, innovation, no. 1(17) (2021): 73–79. http://dx.doi.org/10.35668/2520-6524-2021-1-07.

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The article highlights the modern approach of creating a classification of transport aircraft modifications. The general tendency of successful functioning of aviation business based on modified base planes operation is analyzed. The purpose of the article is to study the world practice of classifications of modifications of transport aircrafts and its adaptation in a single modern approach depending on the modifications of aircraft. It is revealed that the classification of aircraft modifications is based on the classical theory of artificial classifications, which has a number of shortcomings, among which is the erroneous choice of a number of components of many classification levels (subclasses, groups and subgroups). A new concept based on the adopted provisions, taking into account and implementing all five features of the notion “concept” and defining the notion of “aircraft modification” as a change of one or more of the five features of change (functional purpose, scope, flight, technical characteristics, the level of improvement of the technical solution or its unit, economic efficiency) is proposed. Based on the theory of combinatorics, the maximum number of aircraft modifications was determined for five features: 31 modifications and for four single-level features — 15 modifications. Classification levels of varieties, classes, subclasses, groups and subgroups are established, where the varieties is the basis of the highest classification level, and the last four — are referred to equivalent classification levels — classes. The system of coding of modifications of all classes, subclasses, groups and subgroups consisting of letters and numbers is developed and proved. Examples of these encodings are given. The authors of the article came to the conclusion that the proposed principle and approach to the formation of the classifier of aircraft modifications should go through a long process of practical implementation and further improvement at all stages of the product life cycle, starting from the subclass.

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Troshkina, Irina Nikolaevna. "Family Values: Category and Structure." Философская мысль, no. 12 (December 2022): 1–9. http://dx.doi.org/10.25136/2409-8728.2022.12.39219.

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The aim of the work is to pose the problem of studying family values in the focus of category and structure. Research objectives: to consider the classification of the category "family values", to reveal the classification of the structure of family values. The object of the study is family values, the subject of the study is the category, structure of family values. The article discusses approaches, the category of "family values", the structure of family values. The author reveals the categories of "family", "modern or non-family values", "traditional family values", focuses on the component basis of family values. The methodological basis of the research consists of the principles and categories of dialectics, methods of analysis and synthesis, system-structural analysis of social systems. Main conclusions: 1. The category of "family values" is studied by researchers within the framework of intra-family, family-extra-family, personal-family constructs. 2. The structure of family values is represented by three systems with one-term, two-term, three-term bases. The most common is a structure consisting of a single-member classification of the family by family elements (matrimony / parenthood, kinship). The author's main contribution to the research of the topic is to identify approaches to the classification of "family values" and the structure of family values, the components of these systems are revealed. The scope of application of the research results is an addition to the existing scientific knowledge in this field of research, inclusion in the programs of special courses of universities in the areas of socio–cultural dynamics, family studies.

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COSTELLO, FINTAN J. "CLASSIFICATION IN CATEGORY CONJUNCTION: THE LOGIC OF OVEREXTENSION." International Journal on Artificial Intelligence Tools 14, no. 01n02 (February 2005): 61–76. http://dx.doi.org/10.1142/s0218213005001990.

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Traditional approaches to semantics give a set-theoretic account of category conjunction, in which an item will be a member of a conjunction A&B only if it is a member of the single category A and a member of the single category B. However, a number of studies have found that people do not follow this set-theoretic account when classifying items in certain natural-language conjunctions. For example, Hampton1 found that while people tend to classify 'toucans' as non-members of the single category 'pet', they classify 'toucans' as members of the conjunction 'pet bird'. This paper describes a general approach to category conjunction that gives a logically consistent explanation for the occurrence of overextension in these conjunctions. The paper also describes a computational model implementing this approach, and an experiment using conjunctions of controlled, laboratory-learned categories to test this model. This computational model gave a good fit to both classification and overextension results in the experiment. The approach to conjunction implemented in this model may provide a useful tool for AI models of classification, allowing them to reason about category conjunction in a way that reflects human reasoning.

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Prassas, Dimitrios, Aristodemos Kounnamas, Kenko Cupisti, Matthias Schott, Wolfram Trudo Knoefel, and Andreas Krieg. "Prognostic Performance of Alternative Lymph Node Classification Systems for Patients with Medullary Thyroid Cancer: A Single Center Cohort Study." Annals of Surgical Oncology 29, no. 4 (December 10, 2021): 2561–69. http://dx.doi.org/10.1245/s10434-021-11134-3.

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Abstract Background Lymph node ratio (LNR) and the log odds of positive lymph nodes (LODDS) have been proposed as alternative lymph node (LN) classification schemes. Various cut-off values have been defined for each system, with the question of the most appropriate for patients with medullary thyroid cancer (MTC) still remaining open. We aimed to retrospectively compare the predictive impact of different LN classification systems and to define the most appropriate set of cut-off values regarding accurate evaluation of overall survival (OS) in patients with MTC. Methods 182 patients with MTC who were operated on between 1985 and 2018 were extracted from our medical database. Cox proportional hazards regression models and C-statistics were performed to assess the discriminative power of 28 LNR and 28 LODDS classifications and compare them with the N category according to the 8th edition of the AJCC/UICC TNM classification in terms of discriminative power. Regression models were adjusted for age, sex, T category, focality, and genetic predisposition. Results High LNR and LODDS are associated with advanced T categories, distant metastasis, sporadic disease, and male gender. In addition, among 56 alternative LN classifications, only one LNR and one LODDS classification were independently associated with OS, regardless of the presence of metastatic disease. The C-statistic demonstrated comparable results for all classification systems showing no clear superiority over the N category. Conclusion Two distinct alternative LN classification systems demonstrated a better prognostic performance in MTC patients than the N category. However, larger scale studies are needed to further verify our findings.

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Kim, Heesang, Gihun Joo, and Hyeonseung Im. "Product Category Classification using Word Embedding and GRUs." Journal of Korean Institute of Information Technology 19, no. 4 (April 30, 2021): 11–18. http://dx.doi.org/10.14801/jkiit.2021.19.4.11.

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Ahieieva, V. O. "COMIC AS LINGUISTIC CATEGORY: CLASSIFICATION, PARAMETRES AND TRANSLATION." Scientific notes of Taurida National V.I. Vernadsky University, series Philology. Social Communications 3, no. 2 (2020): 56–60. http://dx.doi.org/10.32838/2663-6069/2020.2-3/10.

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26

Bregida, Fedir, Andrii Danko, Valentyn Merzhievsky, Viktor Pinchuk, Yuliya Ponomareva, and Yurii Ryndin. "About classification of wheeled vehicles of category L." Avtoshliakhovyk Ukrayiny, no. 1 (253) ’ 2018 (March 2018): 8–14. http://dx.doi.org/10.33868/0365-8392-2018-1-253-8-14.

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A new classification of L-category vehicles needed to be approved is proposed, harmonized with European Union legislation. Keywords: categories, subcategories, criteria, propulsion (electric engine, heat engine, flywheel, hybrid (microhybrid, hybrid, plug-in hybrid, range extender battery)), weeled running gear, electric vehicle, high voltage.

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Hill, S. I., and A. Doucet. "A Framework for Kernel-Based Multi-Category Classification." Journal of Artificial Intelligence Research 30 (December 12, 2007): 525–64. http://dx.doi.org/10.1613/jair.2251.

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A geometric framework for understanding multi-category classification is introduced, through which many existing 'all-together' algorithms can be understood. The structure enables parsimonious optimisation, through a direct extension of the binary methodology. The focus is on Support Vector Classification, with parallels drawn to related methods. The ability of the framework to compare algorithms is illustrated by a brief discussion of Fisher consistency. Its utility in improving understanding of multi-category analysis is demonstrated through a derivation of improved generalisation bounds. It is also described how this architecture provides insights regarding how to further improve on the speed of existing multi-category classification algorithms. An initial example of how this might be achieved is developed in the formulation of a straightforward multi-category Sequential Minimal Optimisation algorithm. Proof-of-concept experimental results have shown that this, combined with the mapping of pairwise results, is comparable with benchmark optimisation speeds.

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Tian, Xiangtao, Zhengli Zhao, Peng Jiang, Jianxin Wu, and Guoqiang Zhong. "Category Relevance Redirection Network for Few-Shot Classification." IEEE Access 10 (2022): 86733–43. http://dx.doi.org/10.1109/access.2022.3199003.

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Zhang, Lin, and Jian Li Zhang. "Classification Algorithm Based on Category Attribute’s Mathematical Expectation." Advanced Materials Research 659 (January 2013): 103–7. http://dx.doi.org/10.4028/www.scientific.net/amr.659.103.

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The thesis introduced a classification algorithm- CAME which based on the training set’s mathematical expectation of each class attribute for unknown data. This algorithm converted the non-numerical or discrete attributes to the corresponding numerical data first, then calculate the mathematical expectation of data which belonging to different categories of numerical attributes. When a new data is needed to predict its classification, let each attribute’s mathematical expectation with existing categories as coordinate, then calculate the distance from new data attribute to various categories. The new data will belong to the category that has the shortest distance to the new data. This algorithm is not affected by attribute’s property or the number of category, and has high accuracy and good scalability.

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Zhao, Wei, Guangyu Wang, and Bo Peng. "Knowledge Text Classification Based on Virtual Category Tree." Revue d'Intelligence Artificielle 33, no. 1 (May 1, 2019): 15–19. http://dx.doi.org/10.18280/ria.330103.

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Zhang, Lin, Xinhai Liu, Frizo Janssens, Liming Liang, and Wolfgang Glänzel. "Subject clustering analysis based on ISI category classification." Journal of Informetrics 4, no. 2 (April 2010): 185–93. http://dx.doi.org/10.1016/j.joi.2009.11.005.

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Liu, Qiuli, Zechao Li, and Jinhui Tang. "Discriminative supplementary representation learning for novel-category classification." Neurocomputing 398 (July 2020): 469–76. http://dx.doi.org/10.1016/j.neucom.2019.03.100.

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Liu, Rey-Long. "Dynamic category profiling for text filtering and classification." Information Processing & Management 43, no. 1 (January 2007): 154–68. http://dx.doi.org/10.1016/j.ipm.2006.02.008.

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Nguyen, Simone P. "Cross-classification and category representation in children's concepts." Developmental Psychology 43, no. 3 (2007): 719–31. http://dx.doi.org/10.1037/0012-1649.43.3.719.

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Tan, Sunfu, and Yifei Pu. "Frac-Vector: Better Category Representation." Fractal and Fractional 7, no. 2 (January 31, 2023): 132. http://dx.doi.org/10.3390/fractalfract7020132.

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For this paper, we proposed the fractional category representation vector (FV) based on fractional calculus (FC), of which one-hot label is only the special case when the derivative order is 0. FV can be considered as a distributional representation when negative probability is considered. FVs can be used either as a regularization method or as a distributed category representation. They gain significantly in the generalization of classification models and representability in generative adversarial networks with conditions (C-GANs). In image classification, the linear combinations of FVs correspond to the mixture of images and can be used as an independent variable of the loss function. Our experiments showed that FVs can also be used as space sampling, with fewer dimensions and less computational overhead than normal distributions.

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Yang, Shih-Ting, and Chia-Wei Huang. "A Two-Dimensional Webpage Classification Model." International Journal of Data Warehousing and Mining 13, no. 2 (April 2017): 13–44. http://dx.doi.org/10.4018/ijdwm.2017040102.

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Regarding the webpage classification topics, most classification mechanisms may lack of consideration from the webpage article writer's perspective and the display characteristics of the webpage (color, graphic layout). Hence, this paper develops a Two-dimensional Webpage Classification model to analyze the webpage textual information and display characteristics from the perspectives of webpage users and designers. This model is consisted of the Webpage Block Distribution Analysis (WBDA) module, Webpage Emotion Category Determination (WECD) module and Webpage Specialty Category Determination (WSCD) module. Firstly, in WBDA module, the user and designer habits (such as the web browsing movement and the writing perspective of webpage) should be considered by combining with the eye movement tracking and tag-region judgment to determine the critical blocks and information of the webpage. Secondly, in WECD module, the webpage color codes are acquired to calculate the major colors of the webpage, and further determine the emotional category of webpage. Thirdly, the WSCD module analyzes the webpage textual information by integrating the keyword acquisition technology to identify the specialty category of the webpage. After that, the Two-dimensional category of the webpage can be obtained. In addition, this paper develops a web-based system accordingly for case verification to confirm the feasibility of the methodology. The verification results show that firstly for webpage emotion category judgment when 128 webpage files for training are imported into this system, the respondent's emotion evaluation score is increased to above Level 5 and the system recommendation success rate is increased to 75.78%. Secondly, for specialty category determination, when system uses 1010 to 1120 webpage files for training, the system performance can be increased to above 80%. Hence, the developed system has a high-performance level in webpage emotion category and specialty category determination. That is, this paper proposes a methodology of Two-dimensional Webpage Classification to classify the webpage file information contents and the effects on the emotions of the demanders to assist webpage providers in providing webpage suitable for demanders with the generated two-dimensional information of the webpage.

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Shi, Li Jun, Xian Cheng Mao, and Zheng Lin Peng. "Method for Classification of Remote Sensing Images Based on Multiple Classifiers Combination." Applied Mechanics and Materials 263-266 (December 2012): 2561–65. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2561.

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This paper presents a new method for classification of remote sensing image based on multiple classifiers combination. In this method, three supervised classifications such as Mahalanobis Distance, Maximum Likelihood and SVM are selected to sever as the sub-classifications. The simple vote classification, maximum probability category method and fuzzy integral method are combined together according to certain rules. And adopted color infrared aerial images of Huairen country as the experimental object. The results show that the overall classification accuracy was improved by 12% and Kappa coefficient was increased by 0.12 compared with SVM classification which has the highest accuracy in single sub-classifications.

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Sakai, Kazutaka. "Baire's Category Theoretic Classification of Compact Expansive Dynamical Systems." Open Systems & Information Dynamics 09, no. 04 (December 2002): 315–23. http://dx.doi.org/10.1023/a:1021802200549.

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A revised version of the estimation inequality of Akashi [2] is given, and this result is applied to Baire's category theoretic classification of ∊-expansive dynamical systems. Moreover, this classification method is applied to topological classification of shift dynamical systems on finite-dimensional compact domains.

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Cheng, Yu Sheng, Xiang Li, and Mei Wen Ding. "Classification Algorithm Based on Categorical Data Analysis." Applied Mechanics and Materials 333-335 (July 2013): 1292–95. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1292.

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Through compares three methods of Standard Deviation mathematical expectation and variance, a classification algorithm based on the Standard Deviation which in the training set is proposed in this paper. The algorithm first mapped the discrete attribute values to the corresponding values, and calculates Standard Deviation, mathematical expectation and Variance of each attribute in each category. The Standard Deviation, mathematical expectation and Variance of each attribute in each category used as coordinates. When there are new datas need to determine the category, we just need to use the attributes of the new data as coordinates, and calculate its distance to each category, and then the data type is the shortest distance category. Comparison of three methods, the Standard Deviation is the most stable and most accurate. This algorithm has advantages in dealing with the noisy date.

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Dell'Ambrogio, Ivo. "Localizing subcategories in the Bootstrap category of separable C*-algebras." Journal of K-theory 8, no. 3 (September 15, 2010): 493–505. http://dx.doi.org/10.1017/is010008010jkt126.

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AbstractUsing the classical universal coefficient theorem of Rosenberg-Schochet, we prove a simple classification of all localizing subcategories of the Bootstrap category Boot ⊂ KK of separable complex C*-algebras. Namely, they are in a bijective correspondence with subsets of the Zariski spectrum Specℤ of the integers – precisely as for the localizing subcategories of the derived category D(ℤ) of complexes of abelian groups. We provide corollaries of this fact and put it in context with the similar classifications available in the literature.

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Singh, Amritpal, and Sunil Kumar Chhillar. "News Category Classification Using Distinctive Bag of Words and ANN Classifier." International Journal of Emerging Research in Management and Technology 6, no. 6 (June 29, 2018): 311. http://dx.doi.org/10.23956/ijermt.v6i6.288.

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Category classification, for news, is a multi-label text classification problem. The goal is to assign one or more categories to a news article. A standard technique in multi-label text classification is to use a set of binary classifiers. For each category, a classifier is used to give a “yes” or “no” answer on if the category should be assigned to a text. Some of the standard algorithms for text classification that are used for binary classifiers include Naive Bayesian Classifiers, Support Vector Machines, artificial neural networks etc. In this distinctive bag of words have been used as feature set based on high frequency word tokens found in individual category of news. The algorithm presented in this work is based on a keyword extraction algorithm that is capable of dealing with English language in which different news categories i.e. Business, entertainment, politics, sports etc. has been considered. Intra-class news classification has been carried out in which Cricket and Football in sports category has been selected to verify the performance of the algorithm. Experimental results shows high classification rate in describing category of a news document.

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42

RIGOPOULOS, GEORGE, and KOSTAS ANAGNOSTOPOULOS. "FUZZY MULTICRITERIA ASSIGNMENT FOR NOMINAL CLASSIFICATION: METHODOLOGY AND APPLICATION IN EVALUATION OF GREEK BANK'S ELECTRONIC PAYMENT RETAILERS." International Journal of Information Technology & Decision Making 09, no. 03 (May 2010): 437–54. http://dx.doi.org/10.1142/s0219622010003890.

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This paper presents a novel multicriteria procedure for nominal classification problems. Assignment of an action to a category is based on the comparison between action and category's least typical representative, which is considered as the category inclusion threshold. Evaluation is executed considering performance on evaluation criteria and calculation of a fuzzy inclusion relation for every action, generalizing preference relations model with concordance and nondiscordance concepts as used in ELECTRE methods. An application to a classification problem in banking is also presented in order to demonstrate its applicability to similar problems.

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&NA;. "The pregnancy classification of the statins has been changed from category C to category D." Reactions Weekly &NA;, no. 1038 (February 2005): 4. http://dx.doi.org/10.2165/00128415-200510380-00008.

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44

Su, Hao, Zhiping Lin, and Lei Sun. "Extraction of category orthonormal subspace for multi-class classification." Journal of the Franklin Institute 358, no. 9 (June 2021): 5089–112. http://dx.doi.org/10.1016/j.jfranklin.2021.03.029.

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45

Gustavsson, Oscar, Thomas Ziegler, Michael C. Welle, Judith Bütepage, Anastasiia Varava, and Danica Kragic. "Cloth manipulation based on category classification and landmark detection." International Journal of Advanced Robotic Systems 19, no. 4 (July 1, 2022): 172988062211104. http://dx.doi.org/10.1177/17298806221110445.

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Cloth manipulation remains a challenging problem for the robotic community. Recently, there has been an increased interest in applying deep learning techniques to problems in the fashion industry. As a result, large annotated data sets for cloth category classification and landmark detection were created. In this work, we leverage these advances in deep learning to perform cloth manipulation. We propose a full cloth manipulation framework that, performs category classification and landmark detection based on an image of a garment, followed by a manipulation strategy. The process is performed iteratively to achieve a stretching task where the goal is to bring a crumbled cloth into a stretched out position. We extensively evaluate our learning pipeline and show a detailed evaluation of our framework on different types of garments in a total of 140 recorded and available experiments. Finally, we demonstrate the benefits of training a network on augmented fashion data over using a small robotic-specific data set.

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Ahn, Hyojung. "A Study of Civil Unmanned Aerial System Category Classification." Journal of the Korean Society for Aeronautical & Space Sciences 43, no. 7 (July 1, 2015): 657–67. http://dx.doi.org/10.5139/jksas.2015.43.7.657.

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Morgan, Emma L., and Mark K. Johansen. "Comparing methods of category learning: Classification versus feature inference." Memory & Cognition 48, no. 5 (February 20, 2020): 710–30. http://dx.doi.org/10.3758/s13421-020-01022-8.

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48

Akusok, Anton, Yoan Miche, Juha Karhunen, Kaj-Mikael Bjork, Rui Nian, and Amaury Lendasse. "Arbitrary Category Classification of Websites Based on Image Content." IEEE Computational Intelligence Magazine 10, no. 2 (May 2015): 30–41. http://dx.doi.org/10.1109/mci.2015.2405317.

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49

Oliveau, Quentin, and Hichem Sahbi. "Learning Attribute Representations for Remote Sensing Ship Category Classification." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, no. 6 (June 2017): 2830–40. http://dx.doi.org/10.1109/jstars.2017.2665346.

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50

Lee, Jae-Uk, Byeong-Kyu Ko, and Pan-Koo Kim. "News Category Classification Using Mutual Information and Log Normalization." Journal of Korean Institute of Information Technology 14, no. 7 (July 31, 2016): 79. http://dx.doi.org/10.14801/jkiit.2016.14.7.79.

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