基于分類誤差一致性準(zhǔn)則的自適應(yīng)知識遷移
doi: 10.11999/JEIT181054
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南京郵電大學(xué)地理與生物信息學(xué)院 ??南京 ??210023
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南京工業(yè)大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院 南京 211816
Adaptive Knowledge Transfer Based on Classification-error Consensus Regularization
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School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
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Department of Computer and Technology, Nanjing Tech University, Nanjing 211816, China
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摘要: 目前大多數(shù)遷移學(xué)習(xí)方法在利用源域數(shù)據(jù)輔助目標(biāo)域數(shù)據(jù)建模時,通常假設(shè)源域中的數(shù)據(jù)均與目標(biāo)域數(shù)據(jù)相關(guān)。然而在實(shí)際應(yīng)用中,源域中的數(shù)據(jù)并非都與目標(biāo)域數(shù)據(jù)的相關(guān)程度一致,若基于上述假設(shè)往往會導(dǎo)致負(fù)遷移效應(yīng)。為此,該文首先提出分類誤差一致性準(zhǔn)則(CCR),對源域與目標(biāo)域分類誤差的概率分布積分平方誤差進(jìn)行最小化度量。此外,該文提出一種基于CCR的自適應(yīng)知識遷移學(xué)習(xí)方法(CATL),該方法可以快速地從源域中自動確定出與目標(biāo)域相關(guān)的數(shù)據(jù)及其權(quán)重,以輔助目標(biāo)域模型的構(gòu)建,使其能在提高知識遷移效率的同時緩解負(fù)遷移學(xué)習(xí)效應(yīng)。在真實(shí)圖像以及文本數(shù)據(jù)集上的實(shí)驗(yàn)結(jié)果驗(yàn)證了CATL方法的優(yōu)勢。
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關(guān)鍵詞:
- 遷移學(xué)習(xí) /
- 負(fù)遷移 /
- 概率分布 /
- 分類誤差一致性規(guī)則
Abstract: Most current transfer learning methods are modeled by utilizing the source data with the assumption that all data in the source domain are equally related to the target domain. In many practical applications, however, this assumption may induce negative learning effect when it becomes invalid. To tackle this issue, by minimizing the integrated squared error of the probability distribution of the source and target domain classification errors, the Classification-error Consensus Regularization (CCR) is proposed. Furthermore, CCR-based Adaptive knowledge Transfer Learning (CATL) method is developed to quickly determine the correlative source data and the corresponding weights. The proposed method can alleviate the negative transfer learning effect while improving the efficiency of knowledge transfer. The experimental results on the real image and text datasets validate the advantages of the CATL method. -
表 1 圖像數(shù)據(jù)集USPS及MNIST中源域數(shù)據(jù)與目標(biāo)域數(shù)據(jù)的詳細(xì)設(shè)置
任務(wù) 源域數(shù)據(jù) 目標(biāo)域數(shù)據(jù) 正類 負(fù)類 正類 負(fù)類 1 USPS7 USPS9 MNIST7 MNIST9 2 USPS4 USPS9 MNIST4 MNIST9 3 USPS0 USPS6 MNIST0 MNIST6 下載: 導(dǎo)出CSV
表 2 文本數(shù)據(jù)集20-Newsgroups中源域數(shù)據(jù)與目標(biāo)域數(shù)據(jù)的詳細(xì)設(shè)置
任務(wù) 源域數(shù)據(jù) 目標(biāo)域數(shù)據(jù) 正類 負(fù)類 正類 負(fù)類 1 comp.graphics rec.autos comp.os.ms-windows.misc rec.motorcycles 2 comp.sys.ibm.pc.hardware rec.sport.baseball comp.sys.mac.hardware rec.sport.hokey 3 sci.crypt talk.politics.guns sci.electronics talk.politics.mideast 4 sci.med talk.politics.misc sci.space talk.religion.misc 5 rec.autos talk.politics.guns rec.motorcycles talk.politics.mideast 6 rec.sport.baseball talk.politics.misc rec.sport.hokey talk.religion.misc 下載: 導(dǎo)出CSV
表 3 各種算法在圖像任務(wù)上的分類精度
任務(wù) 已標(biāo)注樣本 LSSVM CDSVM ASVM TrAdaBoost STM PRIF CATL2 1 4 0.5287 0.5611 0.5913 0.5799 0.6018 0.6245 0.6359 6 0.5520 0.5800 0.6094 0.6133 0.6298 0.6384 0.6477 8 0.5897 0.6112 0.6266 0.6007 0.6319 0.6421 0.6528 10 0.6030 0.6392 0.6502 0.6213 0.6487 0.6539 0.6672 12 0.6381 0.6461 0.6383 0.6588 0.6643 0.6753 0.6791 14 0.6541 0.6587 0.6754 0.6682 0.6901 0.6982 0.7014 2 4 0.5354 0.5743 0.5998 0.5887 0.5983 0.6223 0.6133 6 0.5897 0.5992 0.6293 0.5903 0.6426 0.6478 0.6520 8 0.6276 0.6387 0.6492 0.6690 0.6803 0.6893 0.6927 10 0.6508 0.6641 0.6843 0.6905 0.7067 0.7029 0.7168 12 0.6892 0.6698 0.6988 0.7123 0.7234 0.7326 0.7387 14 0.7098 0.7156 0.7207 0.7076 0.7266 0.7391 0.7421 3 4 0.6578 0.6903 0.7026 0.6873 0.7235 0.7472 0.7492 6 0.7013 0.7445 0.7529 0.7354 0.7541 0.7632 0.7726 8 0.7452 0.7695 0.7721 0.7455 0.7618 07726 0.7829 10 0.7762 0.7803 0.7789 0.7836 0.7928 0.7918 0.8193 12 0.7923 0.7944 0.8034 0.7994 0.8288 0.8172 0.8301 14 0.8234 0.8213 0.8178 0.8145 0.8397 0.8263 0.8452 下載: 導(dǎo)出CSV
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