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- W2137618937 abstract "We propose an efficient algorithm to directly optimize class average precision (AP) with a Maximal Figure-of-Merit (MFoM) learning scheme. AP is considered as a staircase function with respect to each individual sample score after rank ordering is applied to all samples. A combination of sigmoid functions is then used to approximate AP as a continuously differentiable function of the classified parameters used to compute the sample scores. Compared to pair-wise ranking comparisons, the computational complexity of the proposed MFoM-AP learning algorithm can be substantially reduced when estimating classifier parameters with a gradient descent algorithm. Experiments on the TRECVID 2005 high-level feature extraction task showed that the proposed algorithm can effectively improve the mean average precision (MAP) over 39 concepts from a baseline performance of 0.4039 with MFoM maximizing F1 to 0.4274 with MFoM-AP, while showing significant impromvements for 12 concepts as more than 10%." @default.
- W2137618937 created "2016-06-24" @default.
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- W2137618937 date "2011-09-01" @default.
- W2137618937 modified "2023-09-26" @default.
- W2137618937 title "Optimization of average precision with Maximal Figure-of-Merit Learning" @default.
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- W2137618937 doi "https://doi.org/10.1109/mlsp.2011.6064638" @default.
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