标杆管理
众包
计算机科学
任务(项目管理)
开源
判断
选择(遗传算法)
机器学习
接口(物质)
主动学习(机器学习)
人工智能
人机交互
万维网
程序设计语言
工程类
操作系统
软件
业务
最大气泡压力法
气泡
营销
法学
系统工程
政治学
作者
Matteo Venanzi,Oliver Parson,Alex Rogers,Nicholas R. Jennings
标识
DOI:10.1609/hcomp.v3i1.13256
摘要
We present an open-source toolkit that allows the easy comparison of the performance of active learning methods over a series of datasets. The toolkit allows such strategies to be constructed by combining a judgement aggregation model, task selection method and worker selection method.The toolkit also provides a user interface which allows researchers to gain insight into worker performance and task classification at runtime.
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