Labeled data

수학노트
둘러보기로 가기 검색하러 가기

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. ↑ 1.0 1.1 Labeled Training Sets for Machine Learning
  2. ↑ Labeled Data Set - an overview
  3. ↑ How to Organize Data Labeling for Machine Learning: Approaches and Tools
  4. ↑ 4.0 4.1 4.2 4.3 A Guide to Learning with Limited Labeled Data
  5. ↑ 5.0 5.1 5.2 RenderGAN: Generating Realistic Labeled Data
  6. ↑ 6.0 6.1 6.2 What is Data Labeling & How to Choose a Data Labeling Partner
  7. ↑ Classification Method Utilizing Reliably Labeled Data
  8. ↑ Your first step towards AI — labeled Data!
  9. ↑ 9.0 9.1 What is the Difference Between Labeled and Unlabeled Data?
  10. ↑ Labeled data
  11. ↑ Collecting Labeled Data
  12. ↑ 12.0 12.1 12.2 12.3 Introduction to Labeled Data: What, Why, and How

메타데이터

위키데이터

Spacy 패턴 목록

  • [{'LOWER': 'labeled'}, {'LEMMA': 'datum'}]
원본 주소 "https://wiki.mathnt.net/index.php?title=Labeled_data&oldid=51483"