Learnware： on the future of machine learning

Learnware： on the future of machine learning

Current machine learning techniques have achieved great success; however, there are many deficiencies. First, to train a strong model, a large amount of training examples are required, whereas collecting the data, particularly data with labels, is expensive or even difficult in many real tasks. Seco...

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Veröffentlicht in:Frontiers of Computer Science 2016-08, Vol.10 (4), p.589-590
1. Verfasser: ZHOU, Zhi-Hua
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Computer Science
Machine learning
Perspective
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description Current machine learning techniques have achieved great success; however, there are many deficiencies. First, to train a strong model, a large amount of training examples are required, whereas collecting the data, particularly data with labels, is expensive or even difficult in many real tasks. Second, once a model has been trained, if environment changes, which often happens in real tasks, the model can hardly perform well or even become useless. Third,
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subjects Computer Science
Machine learning
Perspective
title Learnware： on the future of machine learning
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