AdaBoost Meaning: Definition, Examples, and Translations
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AdaBoost
[หษหdษหbuหst ]
Definition
machine learning
AdaBoost is a popular boosting algorithm used in machine learning. It combines multiple weak classifiers to create a strong classifier. In each iteration, it focuses on the training examples that the previous classifiers have misclassified, giving them more weight. This helps the algorithm to learn from its mistakes and improve the overall performance.
Synonyms
Adaptive Boosting.
Which Synonym Should You Choose?
Word | Description / Examples |
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AdaBoost |
Use 'AdaBoost' when referring to the specific algorithm and its implementation in more technical discussions, particularly within the field of machine learning.
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Adaptive Boosting |
A more descriptive name of AdaBoost. Use 'Adaptive Boosting' when explaining to an audience that might not be familiar with the term 'AdaBoost', to highlight the emphasis on the adaptive nature of the boosting algorithm.
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Examples of usage
- AdaBoost is often used in the ensemble learning approach.
- AdaBoost can be sensitive to noisy data.
- AdaBoost is an iterative algorithm.
- AdaBoost is known for its high accuracy.
- AdaBoost can be applied to various classification problems.
Translations
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Origin of 'AdaBoost'
The term AdaBoost stands for Adaptive Boosting and was introduced by Yoav Freund and Robert Schapire in 1996. AdaBoost was designed to improve the performance of weak classifiers in the context of binary classification problems. The algorithm quickly gained popularity due to its effectiveness in boosting the accuracy of machine learning models. Over the years, AdaBoost has become a fundamental technique in the field of machine learning and has inspired various extensions and adaptations.