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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2023.5
- 수록면
- 575 - 578 (4page)
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Multiple Instance learning is gaining more and more popularity over the recent years because of the flexibility in its application, as it fits different and special scenarios. It is basically a variation of supervised learning. Contrary to supervised learning where every instance is properly labelled with a discrete value, MIL supports weakly labelled data with incomplete information. Each instance in training data is not assigned a discrete label rather a single label is assigned to a set of instances known as bag. MIL is applied in many real-life scenarios to solve business problems where labelling data is expensive like medical imaging, computer vision, time series and document classification etc. There are many algorithms for MIL. In this paper we will briefly describe the scenarios where MIL is applicable.
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ABSTRACT
Ⅰ. Introduction
Ⅱ. Literature Review/ Background
Ⅲ. Applications
Ⅳ. Conclusions and Prospect
References
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