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Text Classification from Labeled and Unlabeled Documents using EM
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One method utilized the K-means clustering algorithm to distinguish various profiles of host behaviour.
“One method Utilized the K-means clustering algorithm to distinguish various profiles of host behaviour [176].”
From Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree (2023) · cites this paper as [176]
HMMs can be utilized to categorize specific types of malware.
“HMMs can be utilized to categorize specific types of malware [176].”
From Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree (2023) · cites this paper as [176]
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Metadata
- Authors
- Nigam, K., Mccallum, A. K., Thrun, S., & Mitchell, T.
- Affiliations
- Carnegie Mellon University (US)
- ORCID
- Mitchell, Tom
- Venue
- Machine Learning
- Published
- 2000-05
- Volume
- 39
- Issue
- 2-3
- Pages
- 103-134
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2097089247
- Cited by
- 2,736
- References
- 51
- Citations by year
- 2026: 14 · 2025: 35 · 2024: 49 · 2023: 55 · 2022: 76 · 2021: 91 · 2020: 117 · 2019: 139 · 2018: 125 · 2017: 109 · 2016: 119 · 2015: 122 · 2014: 121 · 2013: 133 · 2012: 142
- FWCI
- 86.20
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Machine Learning (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- text classification, semi-supervised learning, unlabeled documents, expectation-maximization algorithm, Naive Bayes classifier, label propagation, mixture components, data weighting, limited labeled data
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