Experimental Design for Bathymetry Editing

Autor:	Alafate, Julaiti, Freund, Yoav, Sandwell, David T., Tozer, Brook
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	We describe an application of machine learning to a real-world computer assisted labeling task. Our experimental results expose significant deviations from the IID assumption commonly used in machine learning. These results suggest that the common random split of all data into training and testing can often lead to poor performance. Comment: Published as a workshop paper at ICML 2020 Workshop on Real World Experiment Design and Active Learning
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2007.07495 Zobrazit plný text záznamu View this record from Arxiv