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2020년 12월 26일 (토) 05:48 판
노트
- Or if you’re competing in a Kaggle challenge, you’re given a highly curated labeled data set to work with.[1]
- Specifically, a lot of unlabeled data together with a small quantity of labeled data is combined to classify each unlabeled examples.[1]
- In many tasks, there is a paucity of labeled data.[2]
- Inconsistent quality of labeled data.[3]
- Being able to learn with limited labeled data relaxes the stringent labeled data requirement for supervised machine learning.[4]
- A human steps in to provide the label, and the newly labeled data is combined with the initial labeled data to improve the model.[4]
- When we take the learner out of the picture, what is left is a pool of unlabeled data and some labeled data from which a model can be built.[4]
- Active learning sounds tempting – with this approach, it is possible to build applications previously constrained by lack of labeled data.[4]
- If a basic model of the data exists (for example, a 3D model of the human body), it can be used to generate labeled data.[5]
- (2016) used 3D game engines to collect labeled data for image segmentation.[5]
- As they rely on conditional GANs, they need large amounts of labeled data.[5]
- Supervised machine learning algorithms learn from labeled data, data that has been tagged with labels.[6]
- Machine learning models learn to recognize repetitive patterns in labeled data.[6]
- Cons: It is important to ensure that your contractor will not later sell the labeled data to a competitor.[6]
- In this paper, we propose a classification method utilizing reliably labeled data.[7]
- Unfortunately, a certain amount of labeled data is absolutely necessary.[8]
- The more complex the decision function that a machine learning should learn, the more labeled data the system will require.[9]
- These, in particular, are the type and objective of the task, and the availability of labeled data.[9]
- Labeled data is a group of samples that have been tagged with one or more labels.[10]
- Data for which you already know the target answer is called labeled data.[11]
- So we can see that, while labeled data is expensive and hard to get, it also offers a much wider array of possibilities.[12]
- Labeled data makes the training process much more efficient and simple.[12]
- Labeled data is used mostly in supervised learning but also semi-supervised learning, in combination with unlabeled data.[12]
- Your set of labeled data is used to teach the machine with a specific goal in mind.[12]
소스
- ↑ 1.0 1.1 Labeled Training Sets for Machine Learning
- ↑ Labeled Data Set - an overview
- ↑ How to Organize Data Labeling for Machine Learning: Approaches and Tools
- ↑ 4.0 4.1 4.2 4.3 A Guide to Learning with Limited Labeled Data
- ↑ 5.0 5.1 5.2 RenderGAN: Generating Realistic Labeled Data
- ↑ 6.0 6.1 6.2 What is Data Labeling & How to Choose a Data Labeling Partner
- ↑ Classification Method Utilizing Reliably Labeled Data
- ↑ Your first step towards AI — labeled Data!
- ↑ 9.0 9.1 What is the Difference Between Labeled and Unlabeled Data?
- ↑ Labeled data
- ↑ Collecting Labeled Data
- ↑ 12.0 12.1 12.2 12.3 Introduction to Labeled Data: What, Why, and How
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위키데이터
- ID : Q30673951