核化的多视角特权协同随机矢量功能链接网络及其增量学习方法
吴天宇, 王士同

Kernel Multi⁃view Privileged Random vector functional link net⁃work and its incremental learning method
Tianyu Wu, Shitong Wang
表2 KMPRVFL和对比算法在NUS?wide数据集上的性能
Table 2 Binary classification performance of KMPRVFL and other algorithms on NUS?wide dataset
Datasets?ADatasets?BKMPRVFLKRVFLMED?2CPSVM?2V
AccuracySTDAccuracySTDAccuracySTDAccuracySTD
Average84.00%0.02377.63%0.01878.23%0.22179.66%0.024
1buildingscomputer83.86%0.02171.93%0.01577.17%0.01578.36%0.012
2buildingselk86.57%0.02281.01%0.01781.04%0.01782.23%0.015
3buildingsfox90.64%0.01382.28%0.03583.41%0.03584.30%0.029
4buildingshorses85.35%0.03076.42%0.01178.87%0.01179.82%0.015
5buildingsmoon83.59%0.02375.16%0.00981.65%0.00978.52%0.020
6buildingsplants85.98%0.01579.19%0.01381.34%0.01382.25%0.007
7buildingsroad72.68%0.01363.93%0.02662.08%0.02665.22%0.027
8computerelk86.12%0.03377.21%0.02577.47%0.02578.66%0.025
9computerfox85.41%0.03176.39%0.03075.18%0.03076.92%0.024
10computerhorses89.04%0.02278.11%0.01281.52%0.01282.90%0.016
11computermoon83.10%0.02876.45%0.02674.64%0.02677.99%0.029
12computerplants87.00%0.01280.68%0.01479.90%0.01481.34%0.013
13computerroad81.36%0.02174.51%0.03470.62%0.03476.17%0.033
14elkfox75.45%0.03068.95%0.02368.57%0.02370.65%0.026
15elkhorses78.88%0.02176.67%0.02375.73%0.02376.74%0.024
16elkmoon87.77%0.02982.68%0.01383.98%0.01384.53%0.025
17elkplants84.77%0.03382.21%0.00983.75%0.00983.09%0.012
18elkroad82.60%0.02278.90%0.03279.24%0.03279.28%0.032
19foxhorses83.80%0.02176.65%0.01877.68%0.01879.99%0.019
20foxmoon86.22%0.02381.94%0.04482.25%0.04483.92%0.045
21foxplants79.83%0.05779.73%0.01473.88%0.01482.31%0.004
22foxroad84.16%0.03378.81%0.03381.67%0.03379.65%0.031
23horsesmoon88.18%0.03382.56%0.02883.76%0.02884.59%0.032
24horsesplants89.15%0.01185.05%0.01285.12%0.01286.73%0.015
25horsesroad80.14%0.01273.94%0.03574.57%0.03575.67%0.039
26moonplants86.90%0.02881.11%0.03382.40%0.03383.03%0.027
27moonroad81.34%0.02774.71%0.01575.23%0.01576.67%0.029
28plantsroad82.13%0.01776.43%0.03177.68%0.03078.97%0.030