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Learning from Little: Comparison of Classifiers Given Little Training

Diego Marinho de Oliveira

Gen-AI Search, RecSys | ex-SEEK, AI Lead, Data Scientist Manager and ML Engineer Specialist

发布日期: 2017年3月1日

This paper came to mind after a friend gets in trouble with the use of a small amount of manually annotated data ...

"Abstract Many real-world machine learning tasks are faced with the problem of small training sets. Additionally, the class distribution of the training set often does not match the target distribution. In this paper we compare the performance of many learning models on a substantial benchmark of binary text classification tasks having small training sets."

Authors: George Forman , Ira Cohen

Read full paper at https://bit.ly/2m7yTWy

Bonson Sebastian Mampilli Ph.D.

Sr. Data Scientist / Machine Learning Architect / Mentor / Researcher

8 年

Nice... Thanks for sharing Diego!

1 次回应

Monika Kovacs

///

8 年

Thanks Diego, something very useful again as per usual. Have had a quick scan and it's definitely worth reading through in detail.

1 次回应

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Learning from Little: Comparison of Classifiers Given Little Training

Diego Marinho de Oliveira

Gen-AI Search, RecSys | ex-SEEK, AI Lead, Data Scientist Manager and ML Engineer Specialist

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