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Learning ensembles
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Part 2 of ebook "The elements of statistical learning: Data mining, inference, and prediction (Second edition)" provides readers with contents including: Chapter 10 - Boosting and additive trees; Chapter 11 - Neural networks; Chapter 12 - Support vector machines and flexible discriminants; Chapter 13 - Prototype methods and nearest-neighbors; Chapter 14 - Unsupervised learning; Chapter 15 - Random forests; Chapter 16 - Ensemble learning; Chapter 17 - Undirected graphical models; Chapter 18 - High-dimensional problems p ≫ N;...
409p
daonhiennhien
03-07-2024
4
1
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Groundwater potential zoning using Logistics Model Trees based novel ensemble machine learning model
n this work, the main aim is to map the potential zones of groundwater in Central Highlands (Vietnam) using a novel ensemble machine learning model, namely CG-LMT, which is a combination of two advanced techniques, namely Cascade Generalization (CG) and Logistics Model Trees (LMT). For this, a total of 501 wells data and a set of twelve affecting factors were gathered and selected to generate training and testing datasets used for building and validating the model.
10p
dianmotminh02
03-05-2024
9
2
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A damage diagnosis method for trusses based on incomplete free vibration properties utilizing ensemble learning, e.g. Extreme gradient boosting (XGBoost), is presented in this work. Owing to the lack of measurement sensors, modal features are only measured at master degrees of freedom (DOFs) of a few first models instead of all DOFs of a structural system.
12p
viohoyo
25-04-2024
3
2
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Multi-omics data are good resources for prognosis and survival prediction; however, these are difficult to integrate computationally. We introduce DeepProg, a novel ensemble framework of deep-learning and machine-learning approaches that robustly predicts patient survival subtypes using multi-omics data.
15p
vibransone
28-03-2024
6
2
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This study aims to optimize the classification process of providing assistance to Indonesian Telematics Small and Medium Enterprises (SMEs) using a deep learning approach. The data used is the 2016 Economic Census data. The research was conducted comprehensively through the process of comparing performance through several approaches. Deep learning performance shows an optimal accuracy rate of 99.03%, higher than other approaches of the Adaboost and Adaboos-Bagging Ensemble (92.0%), LVQ (93.11%) and Backpropagation (89.1%).
7p
longtimenosee04
06-03-2024
5
1
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Gain-of-function (GOF) variants give rise to increased/novel protein functions whereas loss-of-function (LOF) variants lead to diminished protein function. Experimental approaches for identifying GOF and LOF are generally slow and costly, whilst available computational methods have not been optimized to discriminate between GOF and LOF variants.
19p
vicwell
29-02-2024
3
2
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This study proposes the application of Ensemble Decision Tree Boosted (EDT Boosted) model for forecasting the surface chloride concentration of marine concrete
13p
visharma
20-10-2023
6
4
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Ebook "Rule based systems for big data - A machine learning approach" includes content: Introduction, theoretical preliminaries, generation of classification rules; simplification of classification rules, representation of classification rules, ensemble learning approaches, interpretability analysis,... and other contents.
127p
haojiubujain07
20-09-2023
7
3
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Bài giảng Khai phá dữ liệu (Data mining): Ensemble models, chương này trình bày những nội dung về: introduction; voting; bagging; boosting; stacking and blending; learning ensembles; methods of constructing ensembles; bias-variance tradeoff; simple ensemble techniques;... Mời các bạn cùng tham khảo chi tiết nội dung bài giảng!
90p
diepkhinhchau
18-09-2023
5
5
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Accurate prediction models for spatial prediction of forest fire danger play a vital role in predicting forest fires, which can help prevent and mitigate the detrimental effects of such disasters. This research aims to develop a new ensemble learning model, HHO-RSCDT, capable of accurately predicting spatial patterns of forest fire danger.
19p
viisac
15-09-2023
3
3
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This paper proposes two hybrid ensemble machine learning approaches that integrate random subspace ensemble with bagging and boosting to enhance classification performance with high-dimensional data. Experimental results demonstrate that these methods significantly improve classification accuracy with highdimensional data.
14p
viengels
25-08-2023
6
4
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In this paper, we proposed two types of joint models for answerability prediction and pure-MRC prediction with/ without a dependency mechanism to learn the correlation between a start position and end position in pure-MRC output prediction.
7p
viberkshire
09-08-2023
6
3
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In this paper, we aim at proposing a Vietnamese ASR system for participating in the VLSP 2021 Automatic Speech Recognition Shared Task. The system is based on the Wav2vec 2.0 framework, along with the application of self-training and several data augmentation techniques.
7p
viberkshire
09-08-2023
9
4
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Twitter posts are now recognized as an important source of patient-generated data, providing unique insights into population health. A fundamental step toward incorporating Twitter data in pharmacoepidemiologic research is to automatically recognize medication mentions in tweets.
9p
visteverogers
24-06-2023
5
2
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Develop an approach, One-class-at-a-time, for triaging psychiatric patients using machine learning on textual patient records. Our approach aims to automate the triaging process and reduce expert effort while providing high classification reliability.
7p
visteverogers
24-06-2023
3
2
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This study extends prior research by combining a chronological pharmacovigilance network approach with machine-learning (ML) techniques to predict adverse drug events (ADEs) based on the drugs’ similarities in terms of the proteins they target in the human body. The focus of this research, though, is particularly centered on predicting the drug-ADE associations for a set of 8 common and high-risk ADEs.
11p
visteverogers
24-06-2023
5
2
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To compare Cox models, machine learning (ML), and ensemble models combining both approaches, for prediction of stroke risk in a prospective study of Chinese adults. The results highlight the potential value of expanding the use of ML in clinical practice.
9p
vighostrider
25-05-2023
5
2
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o develop and validate algorithms for predicting 30-day fatal and nonfatal opioid-related overdose using statewide data sources including prescription drug monitoring program data, Hospital Discharge Data System data, and Tennessee (TN) vital records. Current overdose prevention efforts in TN rely on descriptive and retrospective analyses without prognostication.
11p
vighostrider
25-05-2023
5
2
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Identification of drugs, associated medication entities, and interactions among them are crucial to prevent unwanted effects of drug therapy, known as adverse drug events. This article describes our participation to the n2c2 shared-task in extracting relations between medication-related entities in electronic health records.
8p
vighostrider
25-05-2023
5
2
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Ebook Machine learning algorithms: Part 2 presents the following content: Chapter 8: decision trees and ensemble learning, chapter 9: clustering fundamentals, chapter 10: hierarchical clustering, chapter 11: introduction to recommendation systems, chapter 12: introduction to natural language processing, chapter 13: topic modeling and sentiment analysis in NLP, chapter 14: a brief introduction to deep learning and tensorflow, chapter 15: creating a machine learning architecture.
184p
runthenight09
06-05-2023
8
4
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