Text classification from labeled and unlabeled documents using EM

Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm

Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444 · see citing works

Elements of Information Theory

Cover, T. M., & Thomas, J. A. (2001) · Wiley eBooks · doi:10.1002/0471200611 · Cited by 37,139 · see citing works

Text categorization with Support Vector Machines: Learning with many relevant features

Joachims, T. (1998) · Lecture notes in computer science · doi:10.1007/bfb0026683 · Cited by 8,134 · see citing works

Combining labeled and unlabeled data with co-training

Blum, A. L., & Mitchell, T. M. (1998) · Conference on Learning Theory · doi:10.1145/279943.279962 · Cited by 5,597 · see citing works

The EM Algorithm and Extensions

Kushary, D., McLachlan, G. J., & Krishnan, T. (1998) · Technometrics · doi:10.2307/1271189 · Cited by 5,419 · see citing works

A Comparative Study on Feature Selection in Text Categorization

Yang, Y., & Pedersen, J. (1997) · Cited by 4,749 · see citing works

Data mining and knowledge discovery: making sense out of data

Feyyad, U. M. (1996) · IEEE Expert · doi:10.1109/64.539013 · Cited by 4,644 · see citing works

Maximum likelihood estimation from incomplete data via the EM algorithm

Dempster, A. P. (1977) · Journal of the Royal Statistical Society Series A (Statistics in Society) · Cited by 4,506 · see citing works

Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms

Dietterich, T. G. (1998) · Neural Computation · doi:10.1162/089976698300017197 · Cited by 3,840 · see citing works

A comparison of event models for naive bayes text classification

McCallum, A., & Nigam, K. (1998) · Cited by 3,289 · see citing works

On the Optimality of the Simple Bayesian Classifier under Zero-One Loss

Domingos, P., & Pazzani, M. J. (1997) · Machine Learning · doi:10.1023/a:1007413511361 · Cited by 3,115 · see citing works

Relevance feedback in information retrieval

Rocchio, J. J. (1971) · Medical Entomology and Zoology · Cited by 2,680 · see citing works

Learning in Graphical Models

Jordan, M. I. (1998) · Springer eBooks · doi:10.1007/978-94-011-5014-9 · Cited by 2,449 · see citing works

The SMART Retrieval System—Experiments in Automatic Document Processing

Salton, G. (1971) · CERN Document Server (European Organization for Nuclear Research) · Cited by 2,261 · see citing works

Naive (Bayes) at forty: The independence assumption in information retrieval

Lewis, D. D. (1998) · Lecture notes in computer science · doi:10.1007/bfb0026666 · Cited by 2,140 · see citing works

NewsWeeder: Learning to Filter Netnews

Lang, K. (1995) · Elsevier eBooks · doi:10.1016/b978-1-55860-377-6.50048-7 · Cited by 2,119 · see citing works

Relevance weighting of search terms

Robertson, S. E., & Jones, K. S. (1976) · Journal of the American Society for Information Science · doi:10.1002/asi.4630270302 · Cited by 2,094 · see citing works

An Evaluation of Statistical Approaches to Text Categorization

Yang, Y. (1999) · Information Retrieval · doi:10.1023/a:1009982220290 · Cited by 1,945 · see citing works

A sequential algorithm for training text classifiersPreprint

Lewis, D., & Gale, W. A. (1994) · arXiv (Cornell University) · doi:10.5555/188490.188495 · Cited by 1,838 · see citing works

A Universal Prior for Integers and Estimation by Minimum Description Length

Rissanen, J. (1983) · The Annals of Statistics · doi:10.1214/aos/1176346150 · Cited by 1,655 · see citing works

Elements of Information Theory

Phoenix, S. J. D. (1992) · Journal of Modern Optics · doi:10.1080/09500349214551641 · Cited by 1,411 · see citing works

A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization

Joachims, T. (1997) · Cited by 1,256 · see citing works

A Sequential Algorithm for Training Text Classifiers

Lewis, D. D., & Gale, W. A. (1994) · SIGIR ’94 · doi:10.1007/978-1-4471-2099-5_1 · Cited by 1,208 · see citing works

On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality

Friedman, J. H. (1997) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009778005914 · Cited by 1,038 · see citing works

Bayesian classification (AutoClass): theory and results

Cheeseman, P., & Stutz, J. (1996) · Cited by 963 · see citing works

Hierarchically Classifying Documents Using Very Few Words

Koller, D., & Sahami, M. (1997) · Cited by 837 · see citing works

Employing EM and Pool-Based Active Learning for Text Classification

McCallum, A., & Nigam, K. (1998) · Cited by 689 · see citing works

Learning to extract symbolic knowledge from the World Wide Web

Craven, M. W., DiPasquo, D., Freitag, D., McCallum, A., Mitchell, T. M., Nigam, K., & Slattery, S. (1998) · Cited by 677 · see citing works

Developments in Automatic Text Retrieval

Salton, G. (1991) · Science · doi:10.1126/science.253.5023.974 · Cited by 623 · see citing works

Syskill & webert: Identifying interesting web sites

Pazzani, M. J., Muramatsu, J., & Billsus, D. (1996) · Cited by 593 · see citing works

The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon

Shahshahani, B. M., & Landgrebe, D. A. (1994) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/36.312897 · Cited by 577 · see citing works

Supervised learning from incomplete data via an EM approach

Zoubin Ghahramani, A., & Jordan, M. I. (1993) · Cited by 552 · see citing works

An evaluation of phrasal and clustered representations on a text categorization task

Lewis, D. D. (1992) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/133160.133172 · Cited by 544 · see citing works

Committee-Based Sampling For Training Probabilistic Classifiers

Dagan, I., & Engelson, S. P. (1995) · Elsevier eBooks · doi:10.1016/b978-1-55860-377-6.50027-x · Cited by 506 · see citing works

Improving Text Classification by Shrinkage in a Hierarchy of Classes

McCallum, A., Rosenfeld, R., Mitchell, T., & Ng, A. Y. (2022) · doi:10.1184/r1/21708647 · Cited by 471 · see citing works

The EM algorithm and extensions

McLachlan, G. J. 1., & Krishnan, T. 1. (1997) · Cited by 452 · see citing works

Combining classifiers in text categorization

Larkey, L. S., & Croft, W. B. (1996) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/243199.243276 · Cited by 416 · see citing works

A sequential algorithm for training text classifiers

Lewis, D. D. (1995) · ACM SIGIR Forum · doi:10.1145/219587.219592 · Cited by 406 · see citing works

A Mixture of Experts Classifier with Learning Based on Both Labelled and Unlabelled Data

Miller, D. J., & Uyar, H. S. (1996) · neural information processing systems · Cited by 283 · see citing works

Context-sensitive learning methods for text categorization

Cohen, W. W., & Singer, Y. (1996) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/243199.243278 · Cited by 249 · see citing works

On the exponential value of labeled samples

Castelli, V., & Cover, T. M. (1995) · Pattern Recognition Letters · doi:10.1016/0167-8655(94)00074-d · Cited by 202 · see citing works

Expert Network: Effective and Efficient Learning from Human Decisions in Text Categorization and Retrieval

Yang, Y. (1994) · Springer eBooks · doi:10.1007/978-1-4471-2099-5_2 · Cited by 189 · see citing works

Preventing Overfitting of Cross-Validation Data

Ng, A. Y. (1997) · International Conference on Machine Learning · Cited by 165 · see citing works

Threading electronic mail: A preliminary study

Lewis, D. D., & Knowles, K. A. (1997) · Information Processing & Management · doi:10.1016/s0306-4573(96)00063-5 · Cited by 153 · see citing works

Active Learning with Committees for Text Categorization

Liere, R., & Tadepalli, P. V. (1997) · Cited by 143 · see citing works

Best-first Model Merging for Hidden Markov Model InductionPreprint

Stolcke, A., & Omohundro, S. M. (1994) · arXiv (Cornell University) · doi:10.48550/arxiv.cmp-lg/9405017 · Cited by 111 · see citing works

Improving the Mean Field Approximation Via the Use of Mixture Distributions

Jaakkola, T., & Jordan, M. I. (1998) · Springer eBooks · doi:10.1007/978-94-011-5014-9_6 · Cited by 100 · see citing works

A New Metric-Based Approach to Model Selection

Schuurmans, D. (1997) · Cited by 45 · see citing works

Document classification using a finite mixture model

Li, H., & Yamanishi, K. (1997) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.3115/976909.979623 · Cited by 31 · see citing works

Intelligent Agents for Web-based Tasks: An Advice-Taking Approach

SHAVLIK, J. W., & Eliassi‐Rad, T. (1998) · Cited by 25 · see citing works

Document classification using a finite mixture model

Li, H., & Yamanishi, K. (1997) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.3115/979617.979623 · Cited by 10 · see citing works

A survey on transfer learning

Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works

Large-scale automated synthesis of human functional neuroimaging data.

Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C., & Wager, T. D. (2011) · Nature Methods · doi:10.1038/nmeth.1635 · Cited by 3,686 · see citing works

A survey on semi-supervised learning

van Engelen, J. E., & Hoos, H. H. (2020) · Machine Learning · doi:10.1007/s10994-019-05855-6 · Cited by 2,666 · see citing works

A brief introduction to weakly supervised learning

Zhou, Z.-H. (2018) · National Science Review · doi:10.1093/nsr/nwx106 · Cited by 1,899 · see citing works

<b>topicmodels</b> : An <i>R</i> Package for Fitting Topic Models

Grün, B., & Hornik, K. (2011) · Journal of Statistical Software · doi:10.18637/jss.v040.i13 · Cited by 1,072 · see citing works

Adjusting the Outputs of a Classifier to New a <i>Priori</i> Probabilities: A Simple Procedure

Saerens, M., Latinne, P., & Decaestecker, C. (2002) · Neural Computation · doi:10.1162/089976602753284446 · Cited by 361 · see citing works

Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree

Azam, Z., Islam, M. M., & Huda, M. N. (2023) · IEEE Access · doi:10.1109/access.2023.3296444 · Cited by 265 · see citing works

Fuzzy bag-of-words model for document representation

Zhao, R., & Mao, K. (2018) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2017.2690222 · Cited by 252 · see citing works

Applications of text mining within systematic reviews

Thomas, J., McNaught, J., & Ananiadou, S. (2011) · Research Synthesis Methods · doi:10.1002/jrsm.27 · Cited by 212 · see citing works

Learning Bayesian network classifiers for facial expression recognition both labeled and unlabeled data

Cohen, I., Sebe, N., Gozman, F. G., Cirelo, M. C., & Huang, T. S. (2003) · 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. · doi:10.1109/cvpr.2003.1211408 · Cited by 155 · see citing works

Clinical text classification research trends: Systematic literature review and open issues

Mujtaba, G., Shuib, L., Idris, N., Hoo, W. L., Raj, R. G., Khowaja, K., Shaikh, K., & Nweke, H. F. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.09.034 · Cited by 123 · see citing works

Automated assessment of patients’ self-narratives for posttraumatic stress disorder screening using natural language processing and text mining

He, Q., Veldkamp, B. P., Glas, C. A. W., & de Vries, T. (2017) · Assessment · doi:10.1177/1073191115602551 · Cited by 74 · see citing works

Bearing fault diagnosis based on domain adaptation using transferable features under different working conditions

Tong, Z., Li, W., Zhang, B., & Zhang, M. (2018) · Shock and Vibration · doi:10.1155/2018/6714520 · Cited by 64 · see citing works

Open tracing tools: Overview and critical comparison

Janes, A., Li, X., & Lenarduzzi, V. (2023) · Journal of Systems and Software · doi:10.1016/j.jss.2023.111793 · Cited by 28 · see citing works

On the Detection of Shilling Attacks in Federated Collaborative Filtering

Jiang, Y., Zhou, Y., Wu, D., Li, C., & Wang, Y. (2020) · 2020 International Symposium on Reliable Distributed Systems (SRDS) · doi:10.1109/srds51746.2020.00026 · Cited by 22 · see citing works

Bias-variance tradeoff in hybrid generative-discriminative models

Bouchard, G. (2007) · Sixth International Conference on Machine Learning and Applications (ICMLA 2007) · doi:10.1109/icmla.2007.85 · Cited by 16 · see citing works

New Internet search volume-based weighting method for integrating various environmental impacts

Ji, C., & Hong, T. (2015) · Environmental Impact Assessment Review · doi:10.1016/j.eiar.2015.09.008 · Cited by 24 · see citing works

A study of weighting factors of the quadratic performance index

Wakeland, W. (1969) · Journal of the Franklin Institute · doi:10.1016/0016-0032(69)90119-7 · Cited by 5 · see citing works

Extended implied weighting

Goloboff, P. A. (2014) · Cladistics · doi:10.1111/cla.12047 · Cited by 248 · see citing works

High-performance Active Domain Adaption with Fewer Manual Labeling

Li, G., Xiu, H., He, J., & Qi, Y. (2022) · Asia Conference on Algorithms, Computing and Machine Learning (CACML) · doi:10.1109/cacml55074.2022.00080

Weak Adaptation Learning -- Addressing Cross-domain Data Insufficiency with Weak AnnotatorPreprint

Xu, S., Wang, L., Wang, Y., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.07358