.png){kind=icon}
返回
Time-constrained learning
.png){kind=icon}
OA
AI
DOI：10.1016/j.patcog.2023.109672.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Consider a scenario in which we have a huge labeled dataset D and a limited time to train a given learner using D. Since we may not be able to use the whole dataset, how should we proceed? We propose TCT, an algorithm for this task, whose design relies on principles from Machine Teaching. We present an experimental study involving 5 different learners and 20 datasets where we show that TCT consistently outperforms alternative teaching/training methods, namely: (1) Training over batches of random samples, until the time limit is reached; (2) The state-of-the-art Machine Teaching algorithm for black-box learners proposed in [Dasgupta et al., ICML 19], and (3) Stochastic Gradient Descent (when applicable). While our work is primarily practical, we also show that a stripped-down version of TCT has provable guarantees.& COPY; 2023 Elsevier Ltd. All rights reserved.
Keyword:
Machine teaching
Time-constrained learning
Classification methods
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
分享收藏分享收藏A novel active learning framework for classification: Using weighted rank aggregation to achieve multiple query criteria.png)
PATTERN RECOGNITIONOAAI
PATTERN RECOGNITIONIF7.6
OAAI分享收藏SI(FS)2 : Fast simultaneous instance and feature selection for datasets with many featuresSI(FS)2: 具有许多特征的数据集的快速同时实例和特征选择PATTERN RECOGNITIONOAAI
PATTERN RECOGNITIONIF7.6
OAAI分享收藏 没有更多内容
没有更多内容