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Probabilistic Nearest Neighbors Classification
by
Lopes, Hedibert F.
, F., Paulo C. Marques
, Fava, Bruno
in
Algorithms
/ Analysis
/ Approximation
/ Bayesian statistical decision theory
/ Classification
/ Computation
/ Linear programming
/ Methods
/ nearest neighbors classification
/ Neighborhoods
/ NP-completeness
/ Performance prediction
/ Polynomials
/ Prediction models
/ Prediction theory
/ probabilistic machine learning
/ Probabilistic number theory
2024
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Probabilistic Nearest Neighbors Classification
by
Lopes, Hedibert F.
, F., Paulo C. Marques
, Fava, Bruno
in
Algorithms
/ Analysis
/ Approximation
/ Bayesian statistical decision theory
/ Classification
/ Computation
/ Linear programming
/ Methods
/ nearest neighbors classification
/ Neighborhoods
/ NP-completeness
/ Performance prediction
/ Polynomials
/ Prediction models
/ Prediction theory
/ probabilistic machine learning
/ Probabilistic number theory
2024
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Do you wish to request the book?
Probabilistic Nearest Neighbors Classification
by
Lopes, Hedibert F.
, F., Paulo C. Marques
, Fava, Bruno
in
Algorithms
/ Analysis
/ Approximation
/ Bayesian statistical decision theory
/ Classification
/ Computation
/ Linear programming
/ Methods
/ nearest neighbors classification
/ Neighborhoods
/ NP-completeness
/ Performance prediction
/ Polynomials
/ Prediction models
/ Prediction theory
/ probabilistic machine learning
/ Probabilistic number theory
2024
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Journal Article
Probabilistic Nearest Neighbors Classification
2024
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Overview
Analysis of the currently established Bayesian nearest neighbors classification model points to a connection between the computation of its normalizing constant and issues of NP-completeness. An alternative predictive model constructed by aggregating the predictive distributions of simpler nonlocal models is proposed, and analytic expressions for the normalizing constants of these nonlocal models are derived, ensuring polynomial time computation without approximations. Experiments with synthetic and real datasets showcase the predictive performance of the proposed predictive model.
Publisher
MDPI AG
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