Extending the Advise algorithm with variational crowd modelling and entropy-based query selection yields faster learning in small tabular RL tasks, especially highly constrained ones.
A Sequential Algorithm for Training Text Classifiers
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abstract
The ability to cheaply train text classifiers is critical to their use in information retrieval, content analysis, natural language processing, and other tasks involving data which is partly or fully textual. An algorithm for sequential sampling during machine learning of statistical classifiers was developed and tested on a newswire text categorization task. This method, which we call uncertainty sampling, reduced by as much as 500-fold the amount of training data that would have to be manually classified to achieve a given level of effectiveness.
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cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Active Query Selection for Crowd-Based Reinforcement Learning
Extending the Advise algorithm with variational crowd modelling and entropy-based query selection yields faster learning in small tabular RL tasks, especially highly constrained ones.