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Predictive Ensemble Pruning by Expectation Propagation
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Ensemble diversity measures and their application to thinning
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Ensembling neural networks: Many could be better than all
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Optimal Linear Combinations of Neural Networks
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Ensemble learning via negative correlation
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An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization
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Linear Programming Boosting via Column Generation
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An Introduction to MCMC for Machine Learning
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The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance
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Multiple Comparisons Among Means
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Combining Estimates in Regression and Classification
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Approximations of the critical region of the fbietkan statistic
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Optimal linear combination of neural networks for improving classification performance
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Making use of population information in evolutionary artificial neural networks
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Parallel consensual neural networks
inferredcites →https://doi.org/10.1109/icdmw.2006.18openalex-snapshot
A Probabilistic Ensemble Pruning Algorithm
inferredcites →https://doi.org/10.1109/icnn.1997.611672openalex-snapshot
Algorithms for optimal linear combinations of neural networks
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Meta-evolutionary ensembles
inferredcites →https://doi.org/10.1109/tnn.2003.813832openalex-snapshot
A constructive algorithm for training cooperative neural network ensembles
inferredcites →https://doi.org/10.1109/tpami.2006.211openalex-snapshot
Rotation Forest: A New Classifier Ensemble Method
inferredcites →https://doi.org/10.1145/1015330.1015418openalex-snapshot
Predictive automatic relevance determination by expectation propagation
inferredcites →https://doi.org/10.1214/aos/1024691079openalex-snapshot
Arcing classifier (with discussion and a rejoinder by the author)
inferredcites →https://doi.org/10.1613/jair.614openalex-snapshot
Popular Ensemble Methods: An Empirical Study
inferredcites →https://doi.org/10.29172/7c2a6982-6d72-4cd8-bba6-2fccb06a7011openalex-snapshot
Random forests
inferredcites →https://doi.org/10.48550/arxiv.1301.2294openalex-snapshot
Expectation Propagation for approximate Bayesian inference
inferredcites →https://doi.org/10.5555/1005332.1005347openalex-snapshot
Learning Ensembles from Bites: A Scalable and Accurate Approach
inferredcites →https://doi.org/10.5555/1248547.1248548openalex-snapshot
Statistical Comparisons of Classifiers over Multiple Data Sets
inferredcites →https://doi.org/10.5555/1248547.1248595openalex-snapshot
Ensemble Pruning Via Semi-definite Programming
inferredcites →https://doi.org/10.7551/mitpress/1120.003.0054openalex-snapshot
Analysis of Sparse Bayesian Learning
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Confidence in Prediction: An Approach for Dynamic Weighted Ensemble
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Forward Iterative Feature Selection Based on Laplacian Score
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Ensemble Learning and Pruning in Multi-Objective Genetic Programming for Classification with Unbalanced Data
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Energy-Based Metric for Ensemble Selection
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Diversity Regularized Ensemble Pruning
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Classifier Ensemble Using a Heuristic Learning with Sparsity and Diversity
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Explainable online ensemble of deep neural network pruning for time series forecasting
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Ensemble Pruning Based on Objection Maximization With a General Distributed Framework
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Multiclass Probabilistic Classification Vector Machine
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Multiple Kernel k-Means Clustering by Selecting Representative Kernels
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Ensemble Pruning via Margin Maximization
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Probabilistic Classification Vector Machine for Multi-Class Classification
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Online Dynamic Network Embedding
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Multi-Faceted Representation Learning with Hybrid Architecture for Time Series Classification
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