Results 81 to 90 of about 65,886 (196)
Stacked ensemble learning for range-separation parameters [PDF]
Proceedings of the 2021 International Symposium on Molecular Spectroscopy, 2021 Zhou Lin +4 more
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Modeling brand choice using boosted and stacked neural networks [PDF]
, 2005 The brand choice problem in marketing has recently been addressed with methods from computational intelligence such as neural networks. Another class of methods from computational intelligence, the so-called ensemble methods such as boosting and ...
Potharst, R. (Rob) +2 more
core +1 more source
Molecular dynamics simulations of lead clusters
, 2001 Molecular dynamics simulations of nanometer-sized lead clusters have been performed using the Lim, Ong and Ercolessi glue potential (Surf. Sci. {\bf 269/270}, 1109 (1992)).
A. Guinier +37 more
core +1 more source
XStacking: Explanation-Guided Stacked Ensemble Learning
Ensemble Machine Learning (EML) techniques, especially stacking, have been shown to improve predictive performance by combining multiple base models. However, they are often criticized for their lack of interpretability. In this paper, we introduce XStacking, an effective and inherently explainable framework that addresses this limitation by ...
Garouani, Moncef +2 more
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Enhancing Parkinson’s Disease Diagnosis Through Stacking Ensemble-Based Machine Learning Approach
IEEE AccessParkinson’s disease is a progressive neurological condition that affects motor abilities. Common symptoms include tremors, muscle stiffness, and difficulty with coordinated movements.
Riyadh M. Al-Tam +4 more
doaj +1 more source
Gestalt: a Stacking Ensemble for SQuAD2.0
, 2020 We propose a deep-learning system -- for the SQuAD2.0 task -- that finds, or indicates the lack of, a correct answer to a question in a context paragraph. Our goal is to learn an ensemble of heterogeneous SQuAD2.0 models that, when blended properly, outperforms the best model in the ensemble per se.
openaire +2 more sources
General audio tagging with ensembling convolutional neural network and statistical features
, 2018 Audio tagging aims to infer descriptive labels from audio clips. Audio tagging is challenging due to the limited size of data and noisy labels. In this paper, we describe our solution for the DCASE 2018 Task 2 general audio tagging challenge.
Ding, Bo +6 more
core +1 more source
stacks: Stacked Ensemble Modeling with Tidy Data Principles
Journal of Open Source Software, 2022 Simon P. Couch, Max Kuhn
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SeerNet at SemEval-2018 Task 1: Domain Adaptation for Affect in Tweets
, 2018 The paper describes the best performing system for the SemEval-2018 Affect in Tweets (English) sub-tasks. The system focuses on the ordinal classification and regression sub-tasks for valence and emotion.
Duppada, Venkatesh +2 more
core +1 more source
Cascading k-means with Ensemble Learning: Enhanced Categorization of Diabetic Data
Journal of Intelligent Systems, 2012 This paper illustrates the applications of various ensemble methods for enhanced classification accuracy. The case in point is the Pima Indian Diabetic Dataset (PIDD). The computational model comprises of two stages.
Karegowda Asha Gowda +2 more
doaj +1 more source