Maximum Entropy & Support Vector Machines in NLP: Feature-Based Discriminative Learning
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Maximum Entropy & Support Vector Machines in NLP: Feature-Based Discriminative Learning

Michael Brenndoerfer•November 1, 2025•19 min read•4,557 words•Interactive

How Maximum Entropy models and Support Vector Machines revolutionized NLP in 1996 by enabling flexible feature integration for sequence labeling, text classification, and named entity recognition, establishing the supervised learning paradigm

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Reference

BIBTEXAcademic
@misc{maximumentropysupportvectormachinesinnlpfeaturebaseddiscriminativelearning, author = {Michael Brenndoerfer}, title = {Maximum Entropy & Support Vector Machines in NLP: Feature-Based Discriminative Learning}, year = {2025}, url = {https://mbrenndoerfer.com/writing/history-maximum-entropy-svms-nlp}, organization = {mbrenndoerfer.com}, note = {Accessed: 2025-11-02} }
APAAcademic
Michael Brenndoerfer (2025). Maximum Entropy & Support Vector Machines in NLP: Feature-Based Discriminative Learning. Retrieved from https://mbrenndoerfer.com/writing/history-maximum-entropy-svms-nlp
MLAAcademic
Michael Brenndoerfer. "Maximum Entropy & Support Vector Machines in NLP: Feature-Based Discriminative Learning." 2025. Web. 11/2/2025. <https://mbrenndoerfer.com/writing/history-maximum-entropy-svms-nlp>.
CHICAGOAcademic
Michael Brenndoerfer. "Maximum Entropy & Support Vector Machines in NLP: Feature-Based Discriminative Learning." Accessed 11/2/2025. https://mbrenndoerfer.com/writing/history-maximum-entropy-svms-nlp.
HARVARDAcademic
Michael Brenndoerfer (2025) 'Maximum Entropy & Support Vector Machines in NLP: Feature-Based Discriminative Learning'. Available at: https://mbrenndoerfer.com/writing/history-maximum-entropy-svms-nlp (Accessed: 11/2/2025).
SimpleBasic
Michael Brenndoerfer (2025). Maximum Entropy & Support Vector Machines in NLP: Feature-Based Discriminative Learning. https://mbrenndoerfer.com/writing/history-maximum-entropy-svms-nlp
Michael Brenndoerfer

About the author: Michael Brenndoerfer

All opinions expressed here are my own and do not reflect the views of my employer.

Michael currently works as an Associate Director of Data Science at EQT Partners in Singapore, where he drives AI and data initiatives across private capital investments.

With over a decade of experience spanning private equity, management consulting, and software engineering, he specializes in building and scaling analytics capabilities from the ground up. He has published research in leading AI conferences and holds expertise in machine learning, natural language processing, and value creation through data.

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