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Everything about Supervised learning
Machine learning paradigm
In machine learning, supervised learning (SL) is a type of machine learning paradigm where an algorithm learns to map input data to a specific output based on example input-output pairs. This process involves training a statistical model using labeled data, meaning each piece of input data is provided with the correct output. The term "supervised" refers to the role of a teacher or supervisor who provides this training data, guiding the algorithm towards correct predictions. For instance, if you want a model to identify cats in images, supervised learning would involve feeding it many images of cats (inputs) that are explicitly labeled "cat" (outputs). Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Supervised learningA 5-minute orientation
- Skim a recent paper: ProtTrans: Toward Understanding the Language of Life Through Self-Supervised LearningInfluential work since 2022
- Pick up a classic book: The Elements of Statistical LearningThe most-reprinted book on the subject
- Read the landmark paper: Machine learning: supervised methodsCited 316 times
- Go sideways: Statistical learning theoryA closely related topic
- Go sideways: Linear classifierA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
Machine learning: supervised methods
Self-Supervised Learning for Electroencephalography
Supervised tensor learning
Weakly supervised machine learning
Adversarial Dropout for Supervised and Semi-Supervised Learning
Spectral Algorithms for Supervised Learning
Longitudinal self-supervised learning
When semi-supervised learning meets ensemble learning
🆕 Influential since 2022
ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning
Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development
Self-Supervised Contrastive Representation Learning for Semi-Supervised Time-Series Classification
Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning
Semi-supervised Time Series Classification Model with Self-supervised Learning
📚 Books worth reading
The Elements of Statistical LearningTrevor Hastie, Robert Tibshirani · 2001
Supervised Machine LearningTanya Kolosova, Samuel Berestizhevsky · 2020
Semi-supervised learningOlivier Chapelle, Bernhard Schölkopf · 2006
BoostingRobert E. Schapire · 2012
Introduction to semi-supervised learningXiaojin Zhu, Andrew Goldberg · 2008
Marching to different drummersPat Burke Guild · 1985
Self-paced exercise guide to accompany Supervisor's survival kitElwood N. Chapman, Cliff Goodwin · 1982
Supervision as transformationRobin Shohet · 2011
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
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