/LING

Update: February 24, 2023 The new version of Termout.org is now online, so this web site is now obsolete and will soon be dismantled.

Lista de candidatos sometidos a examen:
1) machine learning (*)
(*) Términos presentes en el nuestro glosario de lingüística

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1) Candidate: machine learning

Is in goldstandard

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1
paper corpusSignosTxtLongLines336 - : 2.4. Two approaches in machine learning: Word frequency count and rules

Evaluando al candidato machine learning:

machine learning
Lengua:
Frec: 48
Docs: 14
Nombre propio: / 48 = 0%
Coocurrencias con glosario:
Puntaje: 0.151 = ( + (1+0) / (1+5.61470984411521)));
Candidato aceptado

Referencias bibliográficas encontradas sobre cada término

(Que existan referencias dedicadas a un término es también indicio de terminologicidad.)

machine learning

: Bloedorn, E. & AAani, I. (1998). Using NLP for machine learning of user profiles, Intelligent Data Analysis, 3(2), 3-18. : Burstein, J. & Marcu, D. (2003). A Machine learning approach for identification thesis and conclusion statements in student essays. Computers and the Humanities, 37(4), 455-467. : Caruana, R. & Niculescu-mizil, A. (2006). An empirical comparison of supervised learning algorithms. Ponencia presentada en el International Conference on Machine learning, Pittsburgh, Estados Unidos. : Cortes, C. & Vapnick, V. (1995). Support vector networks. Machine Learning, 20, 273-297. : Jiang, Y., Meng, W. & Clement, Y. (2011): Topic sentiment change analysis. En Machine Learning and Data Mining in Pattern Recognition (pp. 443-457). Springer Verlag: Heidelberg. : Kononenko, I. (1991). Semi-naive bayesian classifier. Ponencia presentada en el European Working Sesion on Learning on Machine Learning, Porto, Portugal. : Leopold, E. & Kindermann, J. (2002). Text categorization with support vector machines. How to represent texts in input space? Machine Learning, 46(1-3) 423-444. : Lewis, D., Yang, Y., Rose, T. & Li. F. (2004). RCV1: A new benchmark collection for text categorization research. Journal of Machine Learning Research, 5, 361-397. : Lewis, D.D. (1998). Naive (Bayes) at forty: The independence assumption in information retrieval. Ponencia presentada en European Conference on Machine Learning, Chemnitz, Alemania. : Lin, D. (1998). An information-theoretic definition of similarity. Proceedings of the 15th International Conference on Machine Learning (pp. 296-304). Madison: Morgan Kaufmann. : McCallum, A. & Nigam, K. (1998). A comparison of event models for naive Bayes text classification. En International conference on machine learning, Workshop on learning for text categorization. Madison, Wisconsin, USA. : Mitchell, T. (1997). Machine learning. Nueva York: WCB/Mcgraw-Hill. : Nam, J., Kim, J., Mencía, E., Gurevych, I. & Fürnkranz, J. (2014). Large-scale multi-label text classification -Revisiting Neural Networks. Machine Learning and Knowledge Discovery in Databases, ECML PKDD, 437-452. : Perkins, S., Lacker, K. & Theiler, J. (2003). Grafting: Fast, incremental feature selection by gradient descent in function space. Journal of Machine Learning Research, 3, 1333-1356. : Quinlan, J. R. (2014). C4. 5: Programs for machine learning. San Mateo, CA: USA. : Read, J., Pfahringer, B., Holmes, G. & Frank, E. (2011). Classifier chains for multi label classification. Machine Learning, 85, 333-359. : Rennie, J., Shih, L., Teevan, J. & Karger, D. (2003). Tackling the poor assumptions of naive Bayes text classifiers. En International conference on machine learning. Washington, DC, USA. : Rivest, R. L. (1987). Learning decision lists. Machine learning, 2(3), 229-246. : Sebastiani, F. (2002). Machine learning in automated text categorization. ACM Computing Survey, 34(1), 1-47. : Soderland, S. (1999). Learning information extraction rules for semi-structured and free text. Machine Learning, 34(1-3), 233-272 : The University of Waikato. (2010a). The University of Waikato Computer Science Department Machine Learning Group, WEKA Manual for Version 3-6-2, [online]. Retrieved from: [68]http://iweb.dl.sourceforge. net/roject/weka/documentation/3.6.x/WekaManual-3-6-2.pdf : Vens, C., Struyf, J., Schietgat, L., Dzeroski, S. & Blockeel, H. (2008). Decision trees for hierarchical multilabel classification. Machine Learning, 73(2), 185-214. : Witten, I. H. & Frank, E. (2005). Data mining: Practical machine learning tools and techniques. San Francisco: Morgan Kaufmann.