Learning from unequally reliable blind ensembles of classifiers
Learning from unequally reliable blind ensembles of classifiers
复制标题
从不同样可靠的分类器盲集合中学习
DOI：
10.1109/globalsip.2017.8308613
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
G. Giannakis
中科院分区：
文献类型：
--
作者：
Panagiotis A. Traganitis;A. Pagés;G. Giannakis
对模式识别和数据分析的兴趣不断增加，这促使开发了许多机器学习算法和工具。对于给定的数据集，执行一个。从分类器的集合中学习的盲目方案，使用联合基质分解是指对每个分类器进行了培训的基础标签的组合。
The rising interest in pattern recognition and data analytics has spurred the development of a plethora of machine learning algorithms and tools. However, as each algorithm has its strengths and weaknesses, one is motivated to judiciously fuse multiple algorithms in order to find the “best” performing one, for a given dataset. Ensemble learning aims to create a highperformance meta-algorithm, by combining the outputs from multiple algorithms. The present work introduces a simple blind scheme for learning from ensembles of classifiers, using joint matrix factorization. Blind refers to the combiner who has no knowledge of the ground-truth labels that each classifier has been trained on. Performance is evaluated on synthetic and real datasets.
DOI：
10.1007/3-540-45014-9
发表时间：
2000-06
期刊：
--
影响因子：
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作者：
Thomas G. Dietterich
通讯作者：
Thomas G. Dietterich