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Everything about AdaBoost
Adaptive boosting based classification algorithm
AdaBoost is a statistical classification meta-algorithm formulated by Yoav Freund and Robert Schapire in 1995, who won the 2003 Gödel Prize for their work. It can be used in conjunction with many types of learning algorithm to improve performance. The output of multiple weak learners is combined into a weighted sum that represents the final output of the boosted classifier. Usually, AdaBoost is presented for binary classification, although it can be generalized to multiple classes or bounded intervals of real values. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: AdaBoostA 5-minute orientation
- Skim a recent paper: An investigation of feature selection methods for soil liquefaction prediction based on tree-based ensemble algorithms using AdaBoost, gradient boosting, and XGBoostInfluential work since 2022
- Pick up a classic book: The Elements of Statistical LearningThe most-reprinted book on the subject
- Read the landmark paper: Multi-class AdaBoostCited 1,593 times
- Go sideways: Boosting (machine learning)A closely related topic
- Go sideways: CoBoostingA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
Multi-class AdaBoost
Soft Margins for AdaBoost
Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery
Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China)
Credit Card Fraud Detection Using AdaBoost and Majority Voting
Logistic Regression, AdaBoost and Bregman Distances
Advance and Prospects of AdaBoost Algorithm
Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping
🆕 Influential since 2022
An investigation of feature selection methods for soil liquefaction prediction based on tree-based ensemble algorithms using AdaBoost, gradient boosting, and XGBoost
Ensemble Machine Learning of Random Forest, AdaBoost and XGBoost for Vertical Total Electron Content Forecasting
An Efficient AdaBoost Algorithm with the Multiple Thresholds Classification
An empowered AdaBoost algorithm implementation: A COVID-19 dataset study
Estimating the heavy metal contents in farmland soil from hyperspectral images based on Stacked AdaBoost ensemble learning
📚 Books worth reading
The Elements of Statistical LearningTrevor Hastie, Robert Tibshirani · 2001
Ensemble methods in data miningGiovanni Seni · 2010
Uncertain schema matchingAvigdor Gal · 2011- 📕Advanced design and manufacturing technology IHun Guo · 2010
- 📕Konpyūta bijon saisentan gaidoYasushi Yagi, Hideo Saitō · 2008
Boosting-based face detection and adaptationCha Zhang · 2010
Neural Information ProcessingTingwen Huang · 2012
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Boosting (machine learning) In machine learning (ML), boosting is an ensemble learning method that combines a set of less accurate models (called "weak learners") to create a single…
- CoBoosting CoBoost is a semi-supervised training algorithm proposed by Collins and Singer in 1999. The original application for the algorithm was the task of named-entity…
- Kernel perceptron In machine learning, the kernel perceptron is a variant of the popular perceptron learning algorithm that can learn kernel machines, i.e. non-linear classifiers…
- BrownBoost BrownBoost is a boosting algorithm that may be robust to noisy datasets. BrownBoost is an adaptive version of the boost by majority algorithm. As is the…
- Gradient boosting Gradient boosting is a machine learning technique based on boosting in a functional space, where the target is pseudo-residuals instead of residuals as…
- Margin classifier In machine learning (ML), a margin classifier is a type of classification model which is able to give an associated distance from the decision boundary…
- Alternating decision tree An alternating decision tree (ADTree) is a machine learning method for classification. It generalizes decision trees and has connections to boosting. An…
- Online machine learning In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update…