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In recent years, there has been an exponential growth in the number of complex documents and texts that require a deeper understanding of machine learning methods to be able to accurately classify texts in many applications.
Pearson, E.S. Bayes’ theorem, examined in the light of experimental sampling. Biometrika
1925
Earlier work this paper cites.
Kullback, S.; Leibler, R.A. On information and sufficiency. Ann. Math. Stat
1951
Earlier work this paper cites.
Morgan, J.N.; Sonquist, J.A. Problems in the analysis of survey data, and a proposal. J. Am. Stat. Assoc
1963
Earlier work this paper cites.
Vapnik, V.; Chervonenkis, A.Y. A class of algorithms for pattern recognition learning. Avtomat. Telemekh
1964
Earlier work this paper cites.
Hill, B.M. Posterior distribution of percentiles: Bayes’ theorem for sampling from a population. J. Am. Stat. Assoc
1968
Earlier work this paper cites.
Kaufmann, S. CUBA: Artificial Conviviality and User-Behaviour Analysis in Web-Feeds. PhD Thesis, Universität Hamburg, Hamburg, Germany 1969
1969
Earlier work this paper cites.
Rocchio, J.J. Relevance feedback in information retrieval. In The SMART Retrieval System: Experiments in Automatic Document Processing
1971
Earlier work this paper cites.
Jones, K.S. Automatic keyword classification for information retrieval. Libr. Q
1971
Earlier work this paper cites.
Sparck Jones, K. A statistical interpretation of term specificity and its application in retrieval. J. Doc
1972
Earlier work this paper cites.
Matthews, B.W. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta (BBA)-Protein Struct
1975
Earlier work this paper cites.
Porter, M.F. An algorithm for suffix stripping. Program
1980
Earlier work this paper cites.
Hanley, J.A.; McNeil, B.J. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology
1982
Earlier work this paper cites.
Gordon, R.S., Jr. An operational classification of disease prevention. Public Health Rep
1983
Earlier work this paper cites.
Hérault, J. Réseaux de neurones à synapses modifiables: Décodage de messages sensoriels composites par une apprentissage non supervisé et permanent. CR Acad. Sci. Paris
1984
Earlier work this paper cites.
Rumelhart, D.E.; Hinton, G.E.; Williams, R.J. Learning Internal Representations by Error Propagation ; Technical Report; California University San Diego, Institute for Cognitive Science: La Jolla, CA, USA, 1985
1985
Earlier work this paper cites.
Buckley, C. Implementation of the SMART Information Retrieval System ; Technical Report; Cornell University: Ithaca, NY, USA, 1985
1985
Earlier work this paper cites.
Johnson, W.B.; Lindenstrauss, J.; Schechtman, G. Extensions of lipschitz maps into Banach spaces. Isr. J. Math
1986
Earlier work this paper cites.
Quinlan, J.R. Induction of decision trees. Mach. Learn
1986
Earlier work this paper cites.
Quinlan, J.R. Simplifying decision trees. Int. J. Man-Mach. Stud
1987
Earlier work this paper cites.
Salton, G.; Buckley, C. Term-weighting approaches in automatic text retrieval. Inf. Process. Manag
1988
Earlier work this paper cites.
Salton, G. Automatic Text Processing: The Transformation, Analysis, and Retrieval of ; Addison-Wesley: Reading, UK, 1989
1989
Earlier work this paper cites.
Schapire, R.E. The strength of weak learnability. Mach. Learn
1990
Earlier work this paper cites.
Jutten, C.; Herault, J. Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture. Signal Process
1991
Earlier work this paper cites.
Safavian, S.R.; Landgrebe, D. A survey of decision tree classifier methodology. IEEE Trans. Syst. Man Cybern
1991
Earlier work this paper cites.
De Mántaras, R.L. A distance-based attribute selection measure for decision tree induction. Mach. Learn
1991
Earlier work this paper cites.
Freund, Y. An improved boosting algorithm and its implications on learning complexity. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, PA, USA, 27–29 July 1992; pp. 391–398
1992
Earlier work this paper cites.
Boser, B.E.; Guyon, I.M.; Vapnik, V.N. A training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, PA, USA, 27–29 July 1992; pp. 144–152
1992
Earlier work this paper cites.
Tokunaga, T.; Makoto, I. Text categorization based on weighted inverse document frequency. Inf. Process. Soc. Jpn. SIGNL
1994
Earlier work this paper cites.
Bengio, Y.; Simard, P.; Frasconi, P. Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw
1994
Earlier work this paper cites.
Morokoff, W.J.; Caflisch, R.E. Quasi-monte carlo integration. J. Comput. Phys
1995
Earlier work this paper cites.
Freund, Y.; Kearns, M.; Mansour, Y.; Ron, D.; Rubinfeld, R.; Schapire, R.E. Efficient algorithms for learning to play repeated games against computationally bounded adversaries. In Proceedings of the 36th Annual Symposium on Foundations of Computer Science, Milwaukee, WI, USA, 23–25 October 1995; pp. 332–341
1995
Earlier work this paper cites.
Magerman, D.M. Statistical decision-tree models for parsing. In Proceedings of the 33rd Annual Meeting on Association for Computational Linguistics, Cambridge, MA, USA, 26–30 June 1995; Association for Computational Linguistics: Stroudsburg, PA, USA, 1995; pp. 276–283
1995
Earlier work this paper cites.
Ho, T.K. Random decision forests. In Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, Canada 14–16 August 1995; Volume 1, pp. 278–282. [ CrossRef ]
1995
Earlier work this paper cites.
Turtle, H. Text retrieval in the legal world. Artif. Intell. Law
1995
Earlier work this paper cites.
Breiman, L. Bagging predictors. Mach. Learn
1996
Earlier work this paper cites.
Hochreiter, S.; Schmidhuber, J. Long short-term memory. Neural Comput
1997
Earlier work this paper cites.
Bradley, A.P. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn
1997
Earlier work this paper cites.
O’Riordan, C.; Sorensen, H. Information filtering and retrieval: An overview. In Proceedings of the 16th Annual International Conference of the IEEE, Atlanta, GA, USA, 28–31 October 1997; p. 42
1997
Earlier work this paper cites.
McCallum, A.; Nigam, K. A comparison of event models for naive bayes text classification. In Proceedings of the AAAI-98 Workshop on Learning for Text Categorization, Madison, WI, USA, 26–27 July 1998; Volume 752, pp. 41–48
1998
Earlier work this paper cites.
Mamitsuka, N.A.H. Query learning strategies using boosting and bagging. In Machine Learning: Proceedings of the Fifteenth International Conference (ICML’98) ; Morgan Kaufmann Pub.: Burlington, MA, USA, 1998; Volume 1
1998
Earlier work this paper cites.
Balakrishnama, S.; Ganapathiraju, A. Linear discriminant analysis-a brief tutorial. Inst. Signal Inf. Process
1998
Earlier work this paper cites.
Weston, J.; Watkins, C. Multi-Class Support Vector Machines ; Technical Report CSD-TR-98-04; Department of Computer Science, Royal Holloway, University of London: Egham, UK, 1998
1998
Earlier work this paper cites.
Maron, O.; Lozano-Pérez, T. A framework for multiple-instance learning. Adv. Neural Inf. Process. Syst
1998
Earlier work this paper cites.
LeCun, Y.; Bottou, L.; Bengio, Y.; Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE
1998
Earlier work this paper cites.
Bauer, E.; Kohavi, R. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Mach. Learn
1999
Earlier work this paper cites.
Breiman, L. Random Forests ; UC Berkeley TR567; University of California: Berkeley, CA, USA, 1999
1999
Earlier work this paper cites.
Yang, Y. An evaluation of statistical approaches to text categorization. Inf. Retr
1999
Earlier work this paper cites.
Mani, I. Advances in Automatic Text Summarization
1999
Earlier work this paper cites.
Kim, Y.H.; Hahn, S.Y.; Zhang, B.T. Text filtering by boosting naive Bayes classifiers. In Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Athens, Greece, 24–28 July 2000; pp. 168–175
2000
Earlier work this paper cites.
Schapire, R.E.; Singer, Y. BoosTexter: A boosting-based system for text categorization. Mach. Learn
2000
Earlier work this paper cites.
Han, E.H.S.; Karypis, G. Centroid-based document classification: Analysis and experimental results. In European Conference on Principles of Data Mining and Knowledge Discovery ; Springer: Berlin/Heidelberg, Germany, 2000; pp. 424–431
2000
Earlier work this paper cites.
Hyvärinen, A.; Oja, E. Independent component analysis: algorithms and applications. Neural Netw
2000
Earlier work this paper cites.
Geurts, P. Some enhancements of decision tree bagging. In European Conference on Principles of Data Mining and Knowledge Discovery ; Springer: Berlin/Heidelberg, Germany, 2000; pp. 136–147
2000
Earlier work this paper cites.
Harrell, F.E. Ordinal logistic regression. In Regression Modeling Strategies
2001
Earlier work this paper cites.
Li, L.; Weinberg, C.R.; Darden, T.A.; Pedersen, L.G. Gene selection for sample classification based on gene expression data: Study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics
2001
Earlier work this paper cites.
Manevitz, L.M.; Yousef, M. One-class SVMs for document classification. J. Mach. Learn. Res
2001
Earlier work this paper cites.
Lafferty, J.; McCallum, A.; Pereira, F.C. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of the 18th International Conference on Machine Learning 2001 (ICML 2001), Williamstown, MA, USA, 28 June–1 July 2001
2001
Earlier work this paper cites.
Hyvärinen, A.; Hoyer, P.O.; Inki, M. Topographic independent component analysis. Neural Comput
2001
Earlier work this paper cites.
Tsuge, S.; Shishibori, M.; Kuroiwa, S.; Kita, K. Dimensionality reduction using non-negative matrix factorization for information retrieval. In Proceedings of the 2001 IEEE International Conference on Systems, Man, and Cybernetics, Tucson, AZ, USA, 7–10 October 2001; Volume 2, pp. 960–965
2001
Earlier work this paper cites.
Bingham, E.; Mannila, H. Random projection in dimensionality reduction: Applications to image and text data. In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 26–29 August 2001; pp. 245–250
2001
Earlier work this paper cites.
Han, E.H.S.; Karypis, G.; Kumar, V. Text categorization using weight adjusted k-nearest neighbor classification. In Pacific-Asia Conference on Knowledge Discovery and Data Mining ; Springer: Berlin/Heidelberg, Germany, 2001; pp. 53–65
2001
Earlier work this paper cites.
Sun, A.; Lim, E.P. Hierarchical text classification and evaluation. In Proceedings of the IEEE International Conference on Data Mining (ICDM 2001), San Jose, CA, USA, 29 November–2 December 2001; pp. 521–528
2001
Earlier work this paper cites.
Mandic, D.P.; Chambers, J.A. Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability
2001
Earlier work this paper cites.
Hand, D.J.; Till, R.J. A simple generalisation of the area under the ROC curve for multiple class classification problems. Mach. Learn
2001
Earlier work this paper cites.
Lock, G. Acute mesenteric ischemia: Classification, evaluation and therapy. Acta Gastro-Enterol. Belg
2002
Earlier work this paper cites.
Hinton, G.E.; Roweis, S.T. Stochastic neighbor embedding. Adv. Neural Inf. Process. Syst
2002
Earlier work this paper cites.
Juan, A.; Vidal, E. On the use of Bernoulli mixture models for text classification. Pattern Recogn
2002
Earlier work this paper cites.
Sahgal, D.; Ramesh, A. On Road Vehicle Detection Using Gabor Wavelet Features with Various Classification Techniques. In Proceedings of the 14th International Conference on Digital Signal Processing Proceedings. DSP 2002 (Cat. No.02TH8628), Santorini, Greece, 1–3 July 2002, doi:10.1109/ICDSP.2002.1028263
2002
Earlier work this paper cites.
Lodhi, H.; Saunders, C.; Shawe-Taylor, J.; Cristianini, N.; Watkins, C. Text classification using string kernels. J. Mach. Learn. Res
2002
Earlier work this paper cites.
Leslie, C.S.; Eskin, E.; Noble, W.S. The spectrum kernel: A string kernel for SVM protein classification. Biocomputing 2002
2002
Earlier work this paper cites.
Eskin, E.; Weston, J.; Noble, W.S.; Leslie, C.S. Mismatch string kernels for SVM protein classification. Adv. Neural Inf. Process. Syst
2002
Earlier work this paper cites.
Sebastiani, F. Machine learning in automated text categorization. ACM Comput. Surv. (CSUR)
2002
Earlier work this paper cites.
Andrews, S.; Tsochantaridis, I.; Hofmann, T. Support vector machines for multiple-instance learning. Adv. Neural Inf. Process. Syst
2002
Earlier work this paper cites.
Hinton, G.E. Training products of experts by minimizing contrastive divergence. Neural Comput
2002
Earlier work this paper cites.
Japkowicz, N.; Stephen, S. The class imbalance problem: A systematic study. Intell. Data Anal
2002
Earlier work this paper cites.
Pang, B.; Lee, L.; Vaithyanathan, S. Thumbs up?: Sentiment classification using machine learning techniques. In ACL-02 Conference on Empirical Methods in Natural Language Processing ; Association for Computational Linguistics: Stroudsburg, PA, USA, 2002; Volume 10, pp. 79–86
2002
Earlier work this paper cites.
Helm, A. Recovery and reclamation: A pilgrimage in understanding who and what we are. In Psychiatric and Mental Health Nursing: The Craft of Caring
2003
Earlier work this paper cites.
Cao, L.; Chua, K.S.; Chong, W.; Lee, H.; Gu, Q. A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine. Neurocomputing
2003
Earlier work this paper cites.
Chakrabarti, S.; Roy, S.; Soundalgekar, M.V. Fast and accurate text classification via multiple linear discriminant projections. VLDB J
2003
Earlier work this paper cites.
Dasgupta, S.; Gupta, A. An elementary proof of a theorem of Johnson and Lindenstrauss. Random Struct. Algorithms
2003
Earlier work this paper cites.
Plisson, J.; Lavrac, N.; Mladenić, D. A rule based approach to word lemmatization. In Proceedings of the 7th International MultiConference Information Society IS 2004, Ljubljana, Slovenia, 13–14 October 2004
2004
Earlier work this paper cites.
Korenius, T.; Laurikkala, J.; Järvelin, K.; Juhola, M. Stemming and lemmatization in the clustering of finnish text documents. In Proceedings of the Thirteenth ACM International Conference on Information and Knowledge Management, Washington, DC, USA, 8–13 November 2004; pp. 625–633
2004
Earlier work this paper cites.
Pauca, V.P.; Shahnaz, F.; Berry, M.W.; Plemmons, R.J. Text mining using non-negative matrix factorizations. In Proceedings of the 2004 SIAM International Conference on Data Mining, Lake Buena Vista, FL, USA, 22–24 April 2004; pp. 452–456
2004
Earlier work this paper cites.
Bloehdorn, S.; Hotho, A. Boosting for text classification with semantic features. In International Workshop on Knowledge Discovery on the Web ; Springer: Berlin/Heidelberg, Germany, 2004; pp. 149–166
2004
Earlier work this paper cites.
Wu, T.F.; Lin, C.J.; Weng, R.C. Probability estimates for multi-class classification by pairwise coupling. J. Mach. Learn. Res
2004
Earlier work this paper cites.
Huang, J.; Ling, C.X. Using AUC and accuracy in evaluating learning algorithms. IEEE Trans. Knowl. Data Eng
2005
Cited alongside, same era.
Sampson, G. The’Language Instinct’Debate: Revised Edition
2005
Cited alongside, same era.
Vempala, S.S. The Random Projection Method
2005
Cited alongside, same era.
Krishnapuram, B.; Carin, L.; Figueiredo, M.A.; Hartemink, A.J. Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE Trans. Pattern Anal. Mach. Intell
2005
Cited alongside, same era.
Tseng, H.; Chang, P.; Andrew, G.; Jurafsky, D.; Manning, C. A conditional random field word segmenter for sighan bakeoff 2005. In Proceedings of the Fourth SIGHAN Workshop on Chinese Language Processing, Jeju Island, Korea, 14–15 October 2005
2005
Cited alongside, same era.
Sutskever, I.; Vinyals, O.; Le, Q.V. Sequence to sequence learning with neural networks. Adv. Neural Inf. Process. Syst
2014
Later among the works it cites.
2014
Later among the works it cites.
Patel, D.; Srivastava, T. Ant Colony Optimization Model for Discrete Tomography Problems. In Proceedings of the Third International Conference on Soft Computing for Problem Solving ; Springer: Berlin/Heidelberg, Germany, 2014; pp. 785–792
2014
Later among the works it cites.
Sahgal, D.; Parida, M. Object Recognition Using Gabor Wavelet Features with Various Classification Techniques. In Proceedings of the Third International Conference on Soft Computing for Problem Solving ; Springer: Berlin/Heidelberg, Germany, 2014; pp. 793–804
2014
Later among the works it cites.
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Graves, A.; Schmidhuber, J. Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw
2005
Cited alongside, same era.
Chapelle, O.; Zien, A. Semi-Supervised Classification by Low Density Separation. In Proceedings of the AISTATS, The Savannah Hotel, Barbados, 6–8 January 2005; pp. 57–64
2005
Cited alongside, same era.
Caropreso, M.F.; Matwin, S. Beyond the bag of words: A text representation for sentence selection. In Conference of the Canadian Society for Computational Studies of Intelligence ; Springer: Berlin/Heidelberg, Germany, 2006; pp. 324–335
2006
Cited alongside, same era.
Sugiyama, M. Local fisher discriminant analysis for supervised dimensionality reduction. In Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, PA, USA, 25–29 June 2006, pp. 905–912
2006
Cited alongside, same era.
Kim, S.B.; Han, K.S.; Rim, H.C.; Myaeng, S.H. Some effective techniques for naive bayes text classification. IEEE Trans. Knowl. Data Eng
2006
Cited alongside, same era.
Frank, E.; Bouckaert, R.R. Naive bayes for text classification with unbalanced classes. In European Conference on Principles of Data Mining and Knowledge Discovery ; Springer: Berlin/Heidelberg, Germany, 2006, pp. 503–510
2006
Cited alongside, same era.
Bo, G.; Xianwu, H. SVM Multi-Class Classification. J. Data Acquis. Process
2006
Cited alongside, same era.
Karamizadeh, S.; Abdullah, S.M.; Halimi, M.; Shayan, J.; Javad Rajabi, M. Advantage and drawback of support vector machine functionality. In Proceedings of the 2014 International Conference on Computer, Communications, and Control Technology (I4CT), Langkawi, Malaysia, 2–4 September 2014; pp. 63–65
2014
Later among the works it cites.
Guo, G. Soft biometrics from face images using support vector machines. In Support Vector Machines Applications
2014
Later among the works it cites.
2014
Later among the works it cites.
2014
Later among the works it cites.
Kingma, D.; Ba, J. Adam: A method for stochastic optimization. arXiv
2014
Later among the works it cites.
Kowsari, K. Investigation of Fuzzyfind Searching with Golay Code Transformations. Ph.D. Thesis, Department of Computer Science, The George Washington University, Washington, DC, USA, 2014
2014
Later among the works it cites.
Zhou, S.; Chen, Q.; Wang, X. Fuzzy deep belief networks for semi-supervised sentiment classification. Neurocomputing
2014
Later among the works it cites.
Lai, S.; Xu, L.; Liu, K.; Zhao, J. Recurrent Convolutional Neural Networks for Text Classification. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, Austin, TX, USA, 25–30 January 2015; Volume 333, pp. 2267–2273
2015
Later among the works it cites.
LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature
2015
Later among the works it cites.
Gupta, G.; Malhotra, S. Text Document Tokenization for Word Frequency Count using Rapid Miner (Taking Resume as an Example). Int. J. Comput. Appl. 2015
2015
Later among the works it cites.
Ng, A. Principal components analysis. Generative Algorithms, Regularization and Model Selection. CS 2015
2015
Later among the works it cites.
Chen, K.; Seuret, M.; Liwicki, M.; Hennebert, J.; Ingold, R. Page segmentation of historical document images with convolutional autoencoders. In Proceedings of the 2015 13th International Conference on Document Analysis and Recognition (ICDAR), Tunis, Tunisia, 23–26 August 2015; pp. 1011–1015
2015
Later among the works it cites.
Geng, J.; Fan, J.; Wang, H.; Ma, X.; Li, B.; Chen, F. High-resolution SAR image classification via deep convolutional autoencoders. IEEE Geosci. Remote Sens. Lett
2015
Later among the works it cites.
2015
Later among the works it cites.
Huang, K. Unconstrained Smartphone Sensing and Empirical Study for Sleep Monitoring and Self-Management. Ph.D. Thesis, University of Massachusetts Lowell, Lowell, MA, USA, 2015
2015
Later among the works it cites.
Zhou, C.; Sun, C.; Liu, Z.; Lau, F. A C-LSTM neural network for text classification. arXiv
2015
Later among the works it cites.
Severyn, A.; Moschitti, A. Learning to rank short text pairs with convolutional deep neural networks. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, Santiago, Chile, 9–13 August 2015; pp. 373–382
2015
Later among the works it cites.
2015
Later among the works it cites.
Zhang, X.; Zhao, J.; LeCun, Y. Character-level convolutional networks for text classification. Adv. Neural Inf. Process. Syst
2015
Later among the works it cites.
Dhuliawala, S.; Kanojia, D.; Bhattacharyya, P. SlangNet: A WordNet like resource for English Slang. In Proceedings of the LREC, Portorož, Slovenia, 23–28 May 2016
2016
Later among the works it cites.
Singh, J.; Gupta, V. Text stemming: Approaches, applications, and challenges. ACM Compu. Surv. (CSUR)
2016
Later among the works it cites.
2016
Later among the works it cites.
Melamud, O.; Goldberger, J.; Dagan, I. context2vec: Learning generic context embedding with bidirectional lstm. In Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, Berlin, Germany, 11–12 August 2016; pp. 51–61
2016
Later among the works it cites.
Jolliffe, I.T.; Cadima, J. Principal component analysis: A review and recent developments. Philos. Trans. R. Soc. A
2016
Later among the works it cites.
Mao, X.; Yuan, C. Stochastic Differential Equations with Markovian Switching
2016
Later among the works it cites.
Goodfellow, I.; Bengio, Y.; Courville, A.; Bengio, Y. Deep Learning
2016
Later among the works it cites.
Sowmya, B.; Srinivasa, K. Large scale multi-label text classification of a hierarchical data set using Rocchio algorithm. In Proceedings of the 2016 International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), Bangalore, India, 6–8 October 2016; pp. 291–296
2016
Later among the works it cites.
Farzi, R.; Bolandi, V. Estimation of organic facies using ensemble methods in comparison with conventional intelligent approaches: A case study of the South Pars Gas Field, Persian Gulf, Iran. Model. Earth Syst. Environ
2016
Later among the works it cites.
Guerin, A. Using Demographic Variables and In-College Attributes to Predict Course-Level Retention for Community College Spanish Students
2016
Later among the works it cites.
Chen, K.; Zhang, Z.; Long, J.; Zhang, H. Turning from TF-IDF to TF-IGM for term weighting in text classification. Expert Syst. Appl
2016
Later among the works it cites.
Jaderberg, M.; Simonyan, K.; Vedaldi, A.; Zisserman, A. Reading text in the wild with convolutional neural networks. Int. J. Comput. Vis
2016
Later among the works it cites.
Yang, Z.; Yang, D.; Dyer, C.; He, X.; Smola, A.J.; Hovy, E.H. Hierarchical Attention Networks for Document Classification. In Proceedings of the HLT-NAACL, San Diego, CA, USA, 12–17 June 2016; pp. 1480–1489
2016
Later among the works it cites.
Seo, P.H.; Lin, Z.; Cohen, S.; Shen, X.; Han, B. Hierarchical attention networks. arXiv
2016
Later among the works it cites.
Gowda, H.S.; Suhil, M.; Guru, D.; Raju, L.N. Semi-supervised text categorization using recursive K-means clustering. In International Conference on Recent Trends in Image Processing and Pattern Recognition ; Springer: Berlin/Heidelberg, Germany, 2016; pp. 217–227
2016
Later among the works it cites.
Lever, J.; Krzywinski, M.; Altman, N. Points of significance: Classification evaluation. Nat. Methods 2016
2016
Later among the works it cites.
Dwivedi, S.K.; Arya, C. Automatic Text Classification in Information retrieval: A Survey. In Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies, Udaipur, India, 4–5 March 2016; p. 131
2016
Later among the works it cites.
Aggarwal, C.C. Content-based recommender systems. In Recommender Systems
2016
Later among the works it cites.
Bergman, P.; Berman, S.J. Represent Yourself in Court: How to Prepare & Try a Winning Case
2016
Later among the works it cites.
Kowsari, K.; Brown, D.E.; Heidarysafa, M.; Jafari Meimandi, K.; Gerber, M.S.; Barnes, L.E. HDLTex: Hierarchical Deep Learning for Text Classification. Machine Learning and Applications (ICMLA). In Proceedings of the 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), Cancun, Mexico, 18–21 December 2017
2017
Later among the works it cites.
Chen, W.; Xie, X.; Wang, J.; Pradhan, B.; Hong, H.; Bui, D.T.; Duan, Z.; Ma, J. A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena
2017
Later among the works it cites.
Dziadek, J.; Henriksson, A.; Duneld, M. Improving Terminology Mapping in Clinical Text with Context-Sensitive Spelling Correction. In Informatics for Health: Connected Citizen-Led Wellness and Population Health
2017
Later among the works it cites.
Liang, H.; Sun, X.; Sun, Y.; Gao, Y. Text feature extraction based on deep learning: A review. EURASIP J. Wirel. Commun. Netw
2017
Later among the works it cites.
Selvi, S.T.; Karthikeyan, P.; Vincent, A.; Abinaya, V.; Neeraja, G.; Deepika, R. Text categorization using Rocchio algorithm and random forest algorithm. In Proceedings of the 2016 Eighth International Conference on Advanced Computing (ICoAC), Chennai, India, 19–21 January 2017; pp. 7–12
2017
Later among the works it cites.
Soheily-Khah, S.; Marteau, P.F.; Béchet, N. Intrusion detection in network systems through hybrid supervised and unsupervised mining process-a detailed case study on the ISCX benchmark data set. HAL 2017
2017
Later among the works it cites.
Singh, R.; Kowsari, K.; Lanchantin, J.; Wang, B.; Qi, Y. GaKCo: A Fast and Scalable Algorithm for Calculating Gapped k-mer string Kernel using Counting. bioRxiv
2017
Later among the works it cites.
Giovanelli, C.; Liu, X.; Sierla, S.; Vyatkin, V.; Ichise, R. Towards an aggregator that exploits big data to bid on frequency containment reserve market. In Proceedings of the 43rd Annual Conference of the IEEE Industrial Electronics Society (IECON 2017), Beijing, China, 29 October–1 November 2017; pp. 7514–7519
2017
Later among the works it cites.
Chen, T.; Xu, R.; He, Y.; Wang, X. Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN. Expert Syst. Appl
2017
Later among the works it cites.
Wang, B.; Xu, J.; Li, J.; Hu, C.; Pan, J.S. Scene text recognition algorithm based on faster RCNN. In Proceedings of the 2017 First International Conference on Electronics Instrumentation & Information Systems (EIIS), Harbin, China, 3–5 June 2017; pp. 1–4
2017
Later among the works it cites.
Shwartz-Ziv, R.; Tishby, N. Opening the black box of deep neural networks via information. arXiv
2017
Later among the works it cites.
2017
Later among the works it cites.
Lampinen, A.K.; McClelland, J.L. One-shot and few-shot learning of word embeddings. arXiv
2017
Later among the works it cites.
2017
Later among the works it cites.
Cao, Z.; Li, W.; Li, S.; Wei, F. Improving Multi-Document Summarization via Text Classification. In Proceedings of the AAAI, San Francisco, CA, USA, 4–9 February 2017; pp. 3053–3059
2017
Later among the works it cites.
Ofoghi, B.; Verspoor, K. Textual Emotion Classification: An Interoperability Study on Cross-Genre data sets. In Australasian Joint Conference on Artificial Intelligence ; Springer: Berlin/Heidelberg, Germany, 2017; pp. 262–273
2017
Later among the works it cites.
Paul, M.J.; Dredze, M. Social Monitoring for Public Health. Synth. Lect. Inf. Concepts Retr. Serv
2017
Later among the works it cites.
Jiang, M.; Liang, Y.; Feng, X.; Fan, X.; Pei, Z.; Xue, Y.; Guan, R. Text classification based on deep belief network and softmax regression. Neural Comput. Appl
2018
Later among the works it cites.
Kowsari, K.; Heidarysafa, M.; Brown, D.E.; Jafari Meimandi, K.; Barnes, L.E. RMDL: Random Multimodel Deep Learning for Classification. In Proceedings of the 2018 International Conference on Information System and Data Mining, Lakeland, FL, USA, 9–11 April 2018; doi:10.1145/3206098.3206111
2018
Later among the works it cites.
Heidarysafa, M.; Kowsari, K.; Brown, D.E.; Jafari Meimandi, K.; Barnes, L.E. An Improvement of Data Classification Using Random Multimodel Deep Learning (RMDL). IJMLC 2018
2018
Later among the works it cites.
Dou, J.; Yamagishi, H.; Zhu, Z.; Yunus, A.P.; Chen, C.W. TXT-tool 1.081-6.1 A Comparative Study of the Binary Logistic Regression (BLR) and Artificial Neural Network (ANN) Models for GIS-Based Spatial Predicting Landslides at a Regional Scale. In Landslide Dynamics: ISDR-ICL Landslide Interactive Teaching Tools
2018
Later among the works it cites.
Nobles, A.L.; Glenn, J.J.; Kowsari, K.; Teachman, B.A.; Barnes, L.E. Identification of Imminent Suicide Risk Among Young Adults using Text Messages. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, Montreal, QC, Canada, 21–26 April 2018; p. 413
2018
Later among the works it cites.
Aggarwal, C.C. Machine Learning for Text
2018
Later among the works it cites.
Pahwa, B.; Taruna, S.; Kasliwal, N. Sentiment Analysis-Strategy for Text Pre-Processing. Int. J. Comput. Appl. 2018
2018
Later among the works it cites.
Mawardi, V.C.; Susanto, N.; Naga, D.S. Spelling Correction for Text Documents in Bahasa Indonesia Using Finite State Automata and Levinshtein Distance Method. EDP Sci. 2018
2018
Later among the works it cites.
Mawardi, V.C.; Rudy, R.; Naga, D.S. Fast and Accurate Spelling Correction Using Trie and Bigram. TELKOMNIKA (Telecommun. Comput. Electron. Control)
2018
Later among the works it cites.
Spirovski, K.; Stevanoska, E.; Kulakov, A.; Popeska, Z.; Velinov, G. Comparison of different model’s performances in task of document classification. In Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics, Novi Sad, Serbia, 25–27 June 2018; p. 10
2018
Later among the works it cites.
2018
Later among the works it cites.
Cox, D.R. Analysis of Binary Data
2018
Later among the works it cites.
Qu, Z.; Song, X.; Zheng, S.; Wang, X.; Song, X.; Li, Z. Improved Bayes Method Based on TF-IDF Feature and Grade Factor Feature for Chinese Information Classification. In Proceedings of the 2018 IEEE International Conference on Big Data and Smart Computing (BigComp), Shanghai, China, 15–17 January 2018; pp. 677–680
2018
Later among the works it cites.
Sanjay, G.P.; Nagori, V.; Sanjay, G.P.; Nagori, V. Comparing Existing Methods for Predicting the Detection of Possibilities of Blood Cancer by Analyzing Health Data. Int. J. Innov. Res. Sci. Technol
2018
Later among the works it cites.
Bansal, H.; Shrivastava, G.; Nhu, N.; Stanciu, L. Social Network Analytics for Contemporary Business Organizations
2018
Later among the works it cites.
Chen, J.; Yan, S.; Wong, K.C. Verbal aggression detection on Twitter comments: Convolutional neural network for short-text sentiment analysis. Neural Comput. Appl
2018
Later among the works it cites.
Hoogeveen, D.; Wang, L.; Baldwin, T.; Verspoor, K.M. Web forum retrieval and text analytics: A survey. Found. Trends® Inf. Retr
2018
Later among the works it cites.
Heidarysafa, M.; Kowsari, K.; Barnes, L.E.; Brown, D.E. Analysis of Railway Accidents’ Narratives Using Deep Learning. In Proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, USA, 17–20 December 2018
2018
Later among the works it cites.
Zhang, J.; Kowsari, K.; Harrison, J.H.; Lobo, J.M.; Barnes, L.E. Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record. IEEE Access
2018
Later among the works it cites.
Kang, M.; Ahn, J.; Lee, K. Opinion mining using ensemble text hidden Markov models for text classification. Expert Syst. Appl
2018
Later among the works it cites.
Johnson, D.; Sinanovic, S. Symmetrizing the Kullback-Leibler Distance. IEEE Trans. Inf. Theory
2019
Closest in time.
Ranjan, M.N.M.; Ghorpade, Y.R.; Kanthale, G.R.; Ghorpade, A.R.; Dubey, A.S. Document Classification Using LSTM Neural Network. J. Data Min. Manag
2019
Closest in time.
Jasim, D.S. Data Mining Approach and Its Application to Dresses Sales Recommendation. Available online: https://www.researchgate.net/profile/Dalia_Jasim/publication/293464737_main_steps_for_doing_data_mining_project_using_weka/links/56b8782008ae44bb330d2583/main-steps-for-doing-data-mining-project-using-weka.pdf (accessed on 23 April 2019)
2019
Closest in time.
Gray, A.; MacDonell, S. Alternatives to Regression Models for Estimating Software Projects. Available online: https://www.researchgate.net/publication/2747623_Alternatives_to_Regression_Models_for_Estimating_Software_Projects (accessed on 23 April 2019)
2019
Closest in time.
Pennebaker, J.; Booth, R.; Boyd, R.; Francis, M. Linguistic Inquiry and Word Count: LIWC2015 ; Pennebaker Conglomerates: Austin, TX, USA, 2015. Available online: www.LIWC.net (accessed on 10 January 2019)
2019
Closest in time.