{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Kurtis Pykes",
  "title_listed": "Semi-Supervised Machine Learning Explained",
  "keywords": "半监督学习",
  "domain": "机器学习",
  "content_type": "深度技术解析",
  "is_paid_flag": "部分免费/会员墙",
  "url": "https://towardsdatascience.com/semi-supervised-machine-learning-explained-c1a6e1e934c7/",
  "publish_date": "2021-08",
  "corpus_stage": "预训练/SFT",
  "notes": "",
  "id": "3093057f12cc",
  "collected_at": "2026-10-02T00:24:09",
  "title": "Semi-Supervised Machine Learning Explained | Towards Data Science",
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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\n# Semi-Supervised Machine Learning Explained\nAnother Way Machines Learn\n[Kurtis Pykes](https://towardsdatascience.com/author/kurtispykes/)\nAugust 11, 20213 min read\n![Photo by JOHN TOWNER on Unsplash](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/08/0nQnkbM4WzoL9n7XR-scaled.jpg)\nPhoto by [JOHN TOWNER](https://unsplash.com/@heytowner?utm_source=medium&utm_medium=referral) on [Unsplash](https://unsplash.com?utm_source=medium&utm_medium=referral)\nMachines can learn in various ways. Supervised learning is a machine learning problem involving learning an input to output mapping function based on example input-output pairs. Unsupervised learning involves learning patterns from unlabeled data. Semi-supervised learning may be seen as a hybrid of both supervised and unsupervised learning.\nEssentially, when we combine a small amount of labeled data to a large amount of unlabeled data during training, we have a semi-supervised machine learning problem. According to [Wikipedia](https://en.wikipedia.org/wiki/Semi-supervised_learning), Semi-supervised learning may be described as a special case of weak supervision [**Source** : Wikipedia].\n> \"Weak Supervision is a branch of machine learning where noisy, limited, or imprecise sources are used to provide supervision signal for labeling large amounts of training data in a supervised learning setting.\" - [Wikipedia](https://en.wikipedia.org/wiki/Weak_supervision).\n### The Data Problem\nSupervised learning models and techniques are commonplace in business. However, building effective models is highly dependent on access to high-quality labeled training data - we've all heard the saying \"Garbage in, garbage out\". The demand for high-quality labeled data often leads to a major roadblock when businesses attempt to approach problems using machine learning. This problem manifests itself in several ways:\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer), [Computer Science](https://roadmap.sh/computer-science) and [Product Manager](https://roadmap.sh/product-manager) roadmaps.\n  * **Insufficient quantity of labeled data** - When a new product is being proposed, or a new industry has come around, a common problem they face is a lack of labeled training data to apply traditional supervised learning approaches. Usually, data scientists would obtain more data but the issue in this scenario is that do so may be impractical, expensive, or impossible without waiting for time to pass so data can be accumulated.\n  * **Insufficient domain expertise to label data** - Some problems can be labeled by absolutely anyone. For example, most people can label images of cats and dogs, but this problem becomes more challenging when labeling the breed of the cat or dog in the image. Getting a domain expert to label training data can quickly become expensive, hence it's often not a viable solution.\n  * **Insufficient time to label and prepare data** - It's known that 60% or more of the time spent working on machine learning problems is dedicated to the preparation of a dataset. When working in a field that deals with fast-evolving problems, collecting and preparing a dataset to build a useful solution quickly enough may be impractical.\n\n\nOverall, collecting high-quality labeled data can quickly put a heavy demand on one's resources, and some companies may not have these resources to accommodate for the demand. Here is where semi-supervised learning comes into play. If we have a small amount of labeled data and a large amount of unlabeled data then we can frame our problem as a semi-supervised machine learning problem.\n### The Types of Semi-supervised Learning\nWhen we are working on a semi-supervised problem, the objective varies depending on the type of learning we wish to perform. We may use semi-supervised learning for inductive learning, transductive learning, or contrast both.\n**Inductive Learning**\nThe goal of inductive learning is to generalize to new data. Thus, inductive learning refers to building a learning algorithm that learns from a labeled training set and generalizes to new data.\n**Transductive Learning**\nThe goal of transductive learning is to transduce information from labeled training datasets to available unlabeled (training) data.\n### Final Thoughts\nSemi-supervised learning falls in between supervised and unsupervised learning. We can conduct semi-supervised learning as either inductive or transductive learning, and both may be performed using a semi-supervised learning algorithm. The way it is done is beyond the scope of this article, but the interested reader may want to read:\n  * [Semi-supervised Learning](http://www.acad.bg/ebook/ml/MITPress-%20SemiSupervised%20Learning.pdf)\n  * [Introduction to Semi-supervised learning](https://www.amazon.co.uk/Introduction-Semi-Supervised-Synthesis-Artificial-Intelligence/dp/1598295470)\n\n\nThanks for Reading!\nIf you enjoyed this article, connect with me by subscribing **** to my** FRE**E [weekly newsletter](https://mailchi.mp/ef1f7700a873/sign-up). Never miss a post I make about Artificial Intelligence, Data Science, and Freelancing.\n### Related Articles\n> [**The Difference Between Classification and Regression in Machine Learning**](https://towardsdatascience.com/the-difference-between-classification-and-regression-in-machine-learning-4ccdb5b18fd3)\n> [**Unsupervised Machine Learning Explained**](https://towardsdatascience.com/unsupervised-machine-learning-explained-1ccc5f20ca29)\n> [**5 Books You Can Read To Learn About Artificial Intelligence**](https://towardsdatascience.com/5-books-you-can-read-to-learn-about-artificial-intelligence-477b5a26277d)\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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