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gptkbp:instanceOf
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gptkb:model
|
|
gptkbp:application
|
bioinformatics
fraud detection
face detection
text classification
|
|
gptkbp:category
|
ensemble learning
|
|
gptkbp:citation
|
high
|
|
gptkbp:combines
|
weak learners
|
|
gptkbp:commonWeakLearner
|
decision stump
|
|
gptkbp:developedBy
|
gptkb:Robert_Schapire
gptkb:Yoav_Freund
|
|
gptkbp:feature
|
iterative process
assigns weights to misclassified samples
can overfit with too many weak learners
focuses on hard-to-classify cases
no parameter tuning required for base learners
reduces bias and variance
works with binary and multiclass classification
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|
gptkbp:form
|
additive modeling
forward stagewise modeling
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gptkbp:fullName
|
gptkb:Adaptive_Boosting
|
|
https://www.w3.org/2000/01/rdf-schema#label
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AdaBoost algorithm
|
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gptkbp:influenced
|
gptkb:CatBoost
gptkb:LightGBM
gptkb:XGBoost
Gradient Boosting Machines
|
|
gptkbp:input
|
labeled data
|
|
gptkbp:introducedIn
|
1996
|
|
gptkbp:limitation
|
sensitive to outliers
sensitive to noisy data
|
|
gptkbp:method
|
boosting
|
|
gptkbp:notablePaperAuthors
|
gptkb:Robert_Schapire
gptkb:Yoav_Freund
|
|
gptkbp:notablePaperYear
|
1997
|
|
gptkbp:notablePublication
|
gptkb:A_Decision-Theoretic_Generalization_of_On-Line_Learning_and_an_Application_to_Boosting
|
|
gptkbp:openSource
|
gptkb:XGBoost
gptkb:Weka
gptkb:scikit-learn
R
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|
gptkbp:output
|
strong classifier
weighted sum of weak learners
|
|
gptkbp:publishedIn
|
gptkb:Journal_of_Computer_and_System_Sciences
|
|
gptkbp:reduces
|
exponential loss
|
|
gptkbp:relatedTo
|
gptkb:Bagging
gptkb:Gradient_Boosting
|
|
gptkbp:supportsAlgorithm
|
supervised learning
|
|
gptkbp:usedFor
|
gptkb:dictionary
regression
|
|
gptkbp:bfsParent
|
gptkb:Robert_Schapire
gptkb:Yoav_Freund
|
|
gptkbp:bfsLayer
|
7
|