Web: http://arxiv.org/abs/2201.10227

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

Data Analytics and Technical support Lead

@ Coupa Software, Inc. | Bogota, Colombia

Data Science Manager

@ Vectra | San Jose, CA

Data Analyst Sr

@ Capco | Brazil - Sao Paulo

Data Scientist (NLP)

@ Builder.ai | London, England, United Kingdom - Remote

Senior Data Analyst

@ BuildZoom | Scottsdale, AZ/ San Francisco, CA/ Remote

Senior Research Scientist, Speech Recognition

@ SoundHound Inc. | Toronto, Canada