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Materie / parole chiave: Random forest; Mathematics; AdaBoost; Statistics; Tree (set theory); Generalization; Support vector machine; Generalization error; Measure (data warehouse); Artificial intelligence; Pattern recognition (psychology); Comp...

Materie / parole chiave: Overfitting; Random forest; Classifier (UML); Computer science; Outlier; Artificial intelligence; Pattern recognition (psychology); Curse of dimensionality; Machine learning; Decision tree; Remote sensing; Data mining

Ensemble methods show improved generalization capabilities that outperforrn those of single larners. lt is generally accepted that, for aggregation to be effective, the individual learners must be as accurate and diverse...

Much of previous attention on decision trees focuses on the splitting criteria and optimization of tree sizes. The dilemma between overfitting and achieving maximum accuracy is seldom resolved. A method to construct a de...

MOTIVATION: Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set. Missi...

"Falls pose a significant health risk to older adults, often resulting in serious injuries and reduced independence. This study explored two machine learning approaches for detecting falls using data from wearable sensor...

The metafor package provides functions for conducting meta-analyses in R. The package includes functions for fitting the meta-analytic fixed- and random-effects models and allows for the inclusion of moderators variables...

El objetivo de este trabajo fue estudiar la asociación entre el gen rhg1 y la resistencia a las razas 3 y 9 de Heterodera glycines (Nematode del Quiste de la Soja - NQS). El cultivar resistente Hartwig fue cruzado con l...

Boosting is one of the most important recent developments in classification methodology. Boosting works by sequentially applying a classification algorithm to reweighted versions of the training data and then taking a we...

Covers supervised learning (prediction) to unsupervised learning. This book contains topics including neural networks, support vector machines, classification trees and boosting

BACKGROUND: Because of specific methodological difficulties in conducting randomized trials, surgical research remains dependent predominantly on observational or non-randomized studies. Few validated instruments are ava...

Funnel plots, and tests for funnel plot asymmetry, have been widely used to examine bias in the results of meta-analyses. Funnel plot asymmetry should not be equated with publication bias, because it has a number of othe...

Abstract Objective: Funnel plots (plots of effect estimates against sample size) may be useful to detect bias in meta-analyses that were later contradicted by large trials. We examined whether a simple test of asymmetry ...