Feature-based Inductive Transfer Learning through Minimum Encoding

Shao, Hao; Suzuki, Einoshin

doi:10.1137/1.9781611972818.23

Cited by 6 publications

(2 citation statements)

References 26 publications

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“…The MDL Principle is built with a solid theoretical foundation that is suitable for model selection to avoid overfitting. It is successfully applied in the inductive transfer learning problem [10], but only confined to one source task and one target task. Put it another way, it is radical to consider that how to select informative knowledge from multiple source tasks can be conceptually regarded the same as the model selection.…”

Section: Introductionmentioning

confidence: 99%

Compact Coding for Hyperplane Classifiers in Heterogeneous Environment

Shao

Tong

Suzuki

2011

Machine Learning and Knowledge Discovery in Databases

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Transfer learning techniques have witnessed a significant development in real applications where the knowledge from previous tasks are required to reduce the high cost of inquiring the labeled information for the target task. However, how to avoid negative transfer which happens due to different distributions of tasks in heterogeneous environment is still a open problem. In order to handle this kind of issue, we propose a Compact Coding method for Hyperplane Classifiers (CCHC) under a two-level framework in inductive transfer learning setting. Unlike traditional methods, we measure the similarities among tasks from the macro level perspective through minimum encoding. Particularly speaking, the degree of the similarity is represented by the relevant code length of the class boundary of each source task with respect to the target task. In addition, informative parts of the source tasks are adaptively selected in the micro level viewpoint to make the choice of the specific source task more accurate. Extensive experiments show the effectiveness of our algorithm in terms of the classification accuracy in both UCI and text data sets.

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Section: Introductionmentioning

confidence: 99%

Compact Coding for Hyperplane Classifiers in Heterogeneous Environment

Shao

Tong

Suzuki

2011

Machine Learning and Knowledge Discovery in Databases

Self Cite

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“…Recently, transfer learning is becoming a popular technology, since it can improve identifying performance based on data similarity (Hamidzadeh, 2015;Pan & Yang, 2010;Shao & Suzuki, 2011;Yang, Mccreadie, & Macdonald, 2017;Yin, Yu, & Sohn, 2018;Yoshida, Kitazono, Ozawa, Sugawara, & Haga, 2016). It has been used in many fields, e.g.…”

mentioning

confidence: 99%

A data transfer method for improving seed identification of maize (Zea mays) haploid breeding based on genetic similarity

Lin

et al. 2019

Plant Breeding

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Maize haploid breeding technology is able to identify haploid seeds non‐destructively, rapidly and at low cost with the help of Near‐infrared (NIR) spectral analysis. However, due to the hybridization of numerous parents and the low production rate of haploid, the haploid data collection becomes a burden for engineering this technology. Biologically, there are considerable similarities between the progeny of the same female parent and different male parents. Based on this advantage, similar spectral data can be transferred when the NIR technology is employed. A revised method of Transfer adaptive boost (TrAdaBoost) is proposed to improve identifying for the backpropagation neural network (BPNN) classifier. To avoid the negative transfer, a screening thresh is used to select out similar data, and the amount of these data are limited to join current training. The results show that the identification performances are improved significantly when the data amount is small. This method shows a high ability to make the seed identification more convenient for engineering maize haploid breeding.

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Active Learning for Text Mining from Crowds

Shao

2017

Advances in Artificial Intelligence: From Theory to Practice

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Feature-based Inductive Transfer Learning through Minimum Encoding

Cited by 6 publications

References 26 publications

Compact Coding for Hyperplane Classifiers in Heterogeneous Environment

Compact Coding for Hyperplane Classifiers in Heterogeneous Environment

A data transfer method for improving seed identification of maize (Zea mays) haploid breeding based on genetic similarity

Active Learning for Text Mining from Crowds

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