Analysis and Optimization of Loss Functions for Multiclass, Top-k, and Multilabel Classification.
IEEE Transactions on Pattern Analysis and Machine Intelligence（2018）
摘要
Top-k error is currently a popular performance measure on large scale image classification benchmarks such as ImageNet and Places. Despite its wide acceptance, our understanding of this metric is limited as most of the previous research is focused on its special case, the top-1 error. In this work, we explore two directions that shed more light on the top-k error. First, we provide an in-depth ana...
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关键词
Support vector machines,Optimization,Calibration,Algorithm design and analysis,Loss measurement,Benchmark testing,Training
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