Search (43 results, page 1 of 3)

  • × theme_ss:"Automatisches Klassifizieren"
  • × year_i:[2010 TO 2020}
  1. HaCohen-Kerner, Y. et al.: Classification using various machine learning methods and combinations of key-phrases and visual features (2016) 0.04 
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    Date
    1. 2.2016 18:25:22
    Language
    e
  2. Zhu, W.Z.; Allen, R.B.: Document clustering using the LSI subspace signature model (2013) 0.02 
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    Date
    23. 3.2013 13:22:36
    Language
    e
  3. Egbert, J.; Biber, D.; Davies, M.: Developing a bottom-up, user-based method of web register classification (2015) 0.02 
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    Date
    4. 8.2015 19:22:04
    Language
    e
  4. Liu, R.-L.: ¬A passage extractor for classification of disease aspect information (2013) 0.02 
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    Date
    28.10.2013 19:22:57
    Language
    e
  5. Cortez, E.; Herrera, M.R.; Silva, A.S. da; Moura, E.S. de; Neubert, M.: Lightweight methods for large-scale product categorization (2011) 0.00 
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    Abstract
    In this article, we present a study about classification methods for large-scale categorization of product offers on e-shopping web sites. We present a study about the performance of previously proposed approaches and deployed a probabilistic approach to model the classification problem. We also studied an alternative way of modeling information about the description of product offers and investigated the usage of price and store of product offers as features adopted in the classification process. Our experiments used two collections of over a million product offers previously categorized by human editors and taxonomies of hundreds of categories from a real e-shopping web site. In these experiments, our method achieved an improvement of up to 9% in the quality of the categorization in comparison with the best baseline we have found.
    Language
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  6. Teich, E.; Degaetano-Ortlieb, S.; Fankhauser, P.; Kermes, H.; Lapshinova-Koltunski, E.: ¬The linguistic construal of disciplinarity : a data-mining approach using register features (2016) 0.00 
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  7. Aphinyanaphongs, Y.; Fu, L.D.; Li, Z.; Peskin, E.R.; Efstathiadis, E.; Aliferis, C.F.; Statnikov, A.: ¬A comprehensive empirical comparison of modern supervised classification and feature selection methods for text categorization (2014) 0.00 
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  8. Barbu, E.: What kind of knowledge is in Wikipedia? : unsupervised extraction of properties for similar concepts (2014) 0.00 
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  9. HaCohen-Kerner, Y.; Beck, H.; Yehudai, E.; Rosenstein, M.; Mughaz, D.: Cuisine : classification using stylistic feature sets and/or name-based feature sets (2010) 0.00 
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  10. Qu, B.; Cong, G.; Li, C.; Sun, A.; Chen, H.: ¬An evaluation of classification models for question topic categorization (2012) 0.00 
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    Abstract
    We study the problem of question topic classification using a very large real-world Community Question Answering (CQA) dataset from Yahoo! Answers. The dataset comprises 3.9 million questions and these questions are organized into more than 1,000 categories in a hierarchy. To the best knowledge, this is the first systematic evaluation of the performance of different classification methods on question topic classification as well as short texts. Specifically, we empirically evaluate the following in classifying questions into CQA categories: (a) the usefulness of n-gram features and bag-of-word features; (b) the performance of three standard classification algorithms (naive Bayes, maximum entropy, and support vector machines); (c) the performance of the state-of-the-art hierarchical classification algorithms; (d) the effect of training data size on performance; and (e) the effectiveness of the different components of CQA data, including subject, content, asker, and the best answer. The experimental results show what aspects are important for question topic classification in terms of both effectiveness and efficiency. We believe that the experimental findings from this study will be useful in real-world classification problems.
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  11. Liu, R.-L.: Context-based term frequency assessment for text classification (2010) 0.00 
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  12. Liu, X.; Yu, S.; Janssens, F.; Glänzel, W.; Moreau, Y.; Moor, B.de: Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database (2010) 0.00 
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  13. Golub, K.: Automated subject classification of textual documents in the context of Web-based hierarchical browsing (2011) 0.00 
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  14. Malo, P.; Sinha, A.; Wallenius, J.; Korhonen, P.: Concept-based document classification using Wikipedia and value function (2011) 0.00 
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  15. Schaalje, G.B.; Blades, N.J.; Funai, T.: ¬An open-set size-adjusted Bayesian classifier for authorship attribution (2013) 0.00 
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  16. Sojka, P.; Lee, M.; Rehurek, R.; Hatlapatka, R.; Kucbel, M.; Bouche, T.; Goutorbe, C.; Anghelache, R.; Wojciechowski, K.: Toolset for entity and semantic associations : Final Release (2013) 0.00 
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  17. Desale, S.K.; Kumbhar, R.: Research on automatic classification of documents in library environment : a literature review (2013) 0.00 
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  18. Ko, Y.: ¬A new term-weighting scheme for text classification using the odds of positive and negative class probabilities (2015) 0.00 
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  19. Kishida, K.: High-speed rough clustering for very large document collections (2010) 0.00 
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  20. Fagni, T.; Sebastiani, F.: Selecting negative examples for hierarchical text classification: An experimental comparison (2010) 0.00 
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