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[کتاب][B] Ensemble methods: foundations and algorithms
ZH Zhou - 2025 - books.google.com
Ensemble methods that train multiple learners and then combine them to use, with Boosting
and Bagging as representatives, are well-known machine learning approaches. It has …
and Bagging as representatives, are well-known machine learning approaches. It has …
An optimal pruning algorithm of classifier ensembles: dynamic programming approach
In recent years, classifier ensemble techniques have drawn the attention of many
researchers in the machine learning research community. The ultimate goal of these …
researchers in the machine learning research community. The ultimate goal of these …
Fake news detection
Information preciseness on Internet, especially on social media, is an increasingly important
concern, but web-scale data hampers, ability to identify, evaluate and correct such data, or …
concern, but web-scale data hampers, ability to identify, evaluate and correct such data, or …
A semantics aware random forest for text classification
The Random Forest (RF) classifiers are suitable for dealing with the high dimensional noisy
data in text classification. An RF model comprises a set of decision trees each of which is …
data in text classification. An RF model comprises a set of decision trees each of which is …
Diversity regularized ensemble pruning
Diversity among individual classifiers is recognized to play a key role in ensemble, however,
few theoretical properties are known for classification. In this paper, by focusing on the …
few theoretical properties are known for classification. In this paper, by focusing on the …
When does diversity help generalization in classification ensembles?
Ensembles, as a widely used and effective technique in the machine learning community,
succeed within a key element—“diversity.” The relationship between diversity and …
succeed within a key element—“diversity.” The relationship between diversity and …
Ensemble methods for multi-label classification
Ensemble methods have been shown to be an effective tool for solving multi-label
classification tasks. In the RAndom k-labELsets (RAKEL) algorithm, each member of the …
classification tasks. In the RAndom k-labELsets (RAKEL) algorithm, each member of the …
Virtual multiphase flow metering using diverse neural network ensemble and adaptive simulated annealing
Real-time production monitoring in oil and gas industry has become very significant
particularly as fields become economically marginal and reservoirs deplete. Virtual flow …
particularly as fields become economically marginal and reservoirs deplete. Virtual flow …
A soft-voting ensemble based co-training scheme using static selection for binary classification problems
In recent years, a forward-looking subfield of machine learning has emerged with important
applications in a variety of scientific fields. Semi-supervised learning is increasingly being …
applications in a variety of scientific fields. Semi-supervised learning is increasingly being …
Pareto ensemble pruning
Ensemble learning is among the state-of-the-art learning techniques, which trains and
combines many base learners. Ensemble pruning removes some of the base learners of an …
combines many base learners. Ensemble pruning removes some of the base learners of an …