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Comparison of Machine Learning Methods: Conclusions and Future Work, and References
Manage episode 427539657 series 3474148
This story was originally published on HackerNoon at: https://hackernoon.com/comparison-of-machine-learning-methods-conclusions-and-future-work-and-references.
This study proposes a set of carefully curated linguistic features for shallow machine learning methods and compares their performance with deep language models
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #natural-language-processing, #machine-learning, #machine-learning-methods, #shallow-machine-learning, #ensemble-machine-learning, #deep-language-models, #types-of-machine-learning, #machine-learning-assignment, and more.
This story was written by: @hashfunction. Learn more about this writer by checking @hashfunction's about page, and for more stories, please visit hackernoon.com.
This study proposes a set of carefully curated linguistic features for shallow machine learning methods and compares their performance with deep language models.
290 episode
Manage episode 427539657 series 3474148
This story was originally published on HackerNoon at: https://hackernoon.com/comparison-of-machine-learning-methods-conclusions-and-future-work-and-references.
This study proposes a set of carefully curated linguistic features for shallow machine learning methods and compares their performance with deep language models
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #natural-language-processing, #machine-learning, #machine-learning-methods, #shallow-machine-learning, #ensemble-machine-learning, #deep-language-models, #types-of-machine-learning, #machine-learning-assignment, and more.
This story was written by: @hashfunction. Learn more about this writer by checking @hashfunction's about page, and for more stories, please visit hackernoon.com.
This study proposes a set of carefully curated linguistic features for shallow machine learning methods and compares their performance with deep language models.
290 episode
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