all AI news
Cold Start Active Learning Strategies in the Context of Imbalanced Classification. (arXiv:2201.10227v1 [cs.LG])
Jan. 26, 2022, 2:11 a.m. | Etienne Brangbour, Pierrick Bruneau, Thomas Tamisier, Stéphane Marchand-Maillet
cs.LG updates on arXiv.org arxiv.org
We present novel active learning strategies dedicated to providing a solution
to the cold start stage, i.e. initializing the classification of a large set of
data with no attached labels. Moreover, proposed strategies are designed to
handle an imbalanced context in which random selection is highly inefficient.
Specifically, our active learning iterations address label scarcity and
imbalance using element scores, combining information extracted from a
clustering structure to a label propagation model. The strategy is illustrated
by a case study …
active learning arxiv classification cold start learning strategies
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Senior Machine Learning Engineer
@ GPTZero | Toronto, Canada
ML/AI Engineer / NLP Expert - Custom LLM Development (x/f/m)
@ HelloBetter | Remote
Doctoral Researcher (m/f/div) in Automated Processing of Bioimages
@ Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI) | Jena
Seeking Developers and Engineers for AI T-Shirt Generator Project
@ Chevon Hicks | Remote
Principal Data Architect - Azure & Big Data
@ MGM Resorts International | Home Office - US, NV
GN SONG MT Market Research Data Analyst 11
@ Accenture | Bengaluru, BDC7A