a novel support vector machine ensemble model for

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A novel support vector machine ensemble model for ...

Apr 01, 2020  In the present article, a novel support vector machine ensemble (ESVM) model was proposed to estimate f-CaO content, with following features and advantages: (1) ESVM model consists of several base SVM regressors. Each base SVM regressor was trained using clustered dataset based on fuzzy c-means clustering algorithm.

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A novel least squares support vector machine ensemble ...

Jun 15, 2013  In this paper, a novel least squares support vector machine (LSSVM)-based ensemble learning paradigm is proposed to predict NO x emission of a coal-fired boiler using real operation data. In view of the plant data characteristics, a soft fuzzy c-means cluster algorithm is proposed to decompose the original data and guarantee the diversity of ...

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A novel ensemble support vector machine model for land ...

Apr 12, 2019  A novel ensemble support vector machine model for land cover classification. Ying Liu and Lihua Huang. International Journal of Distributed Sensor Networks 2019 15: 4. Download Citation. If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager ...

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A novel support vector machine ensemble model for ...

Request PDF A novel support vector machine ensemble model for estimation of free lime content in cement clinkers Free lime (f-CaO) content is a crucial quality parameter for cement clinkers in ...

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A novel optimal support vector machine ensemble model for ...

Oct 01, 2016  A novel ensemble method based on principal component analysis (PCA), genetic algorithm (GA) and support vector machine (SVM) implemented in MATLAB® is presented for establishing the NO X emissions prediction model for a diesel engine for both steady and transient operating states. The different stages of data preprocessing, modeling, optimization and prediction were

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International Journal of Distributed A novel ensemble ...

A novel ensemble support vector machine model for land cover classification Ying Liu1 and Lihua Huang2 Abstract Nowadays, support vector machines are widely applied to land cover classification although this method is sensitive to parameter selection and noise samples. AdaBoost is an effective approach to find a highly accurate classifier by ...

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A novel optimal support vector machine ensemble model for ...

A novel ensemble method based on principal component analysis (PCA), genetic algorithm (GA) and support vector machine (SVM) implemented in MATLAB Ò is presented for establishing the NO X emissions prediction model for a diesel engine for both steady

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A novel support vector machine ensemble model for ...

A novel support vector machine ensemble model for estimation of free lime content in cement clinkers Highlights•A novel SVM ensemble model for cement clinker quality estimation is developed.•Process features at various operating conditions are captured by the model.•The effect of time lag between inputs and output is considered in the model.•The model is compared with soft sensor ...

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EnsembleSVM: A Library for Ensemble Learning Using Support ...

Journal of Machine Learning Research 15 (2014) 141-145 Submitted 4/13; Revised 10/13; Published 1/14 EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines Marc Claesen [email protected] KU Leuven, ESAT { STADIUS/iMinds Future Health Kasteelpark Arenberg 10, box 2446 3001 Leuven, Belgium Frank De Smet frank ...

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Ensemble Learning with Support Vector Machines and ...

Dec 14, 2020  1. Support Vector Machines. The Support Vector Machine algorithm is one of the most powerful one out there in terms of classification. It is based on the idea of getting the largest margin (distance) between the points of the dataset (in particular a set of them, call support vectors) and the separation hyperplane.

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A novel support vector machine ensemble model for ...

Request PDF A novel support vector machine ensemble model for estimation of free lime content in cement clinkers Free lime (f-CaO) content is a crucial quality parameter for cement clinkers in ...

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(PDF) A novel ensemble support vector machine model for ...

A novel ensemble support vector machine model for land cover classification April 2019 International Journal of Distributed Sensor Networks 15(4):155014771984273

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A novel optimal support vector machine ensemble model for ...

A novel ensemble method based on principal component analysis (PCA), genetic algorithm (GA) and support vector machine (SVM) implemented in MATLAB Ò is presented for establishing the NO X emissions prediction model for a diesel engine for both steady

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A novel approach using pharmacophore ensemble/support ...

A novel approach by using a panel of plausible pharmacophore hypothesis candidates to constitute the pharmacophore ensemble (PhE) and subject them to regression by support vector machine (SVM) has been developed for predicting the liability of human ether-a-go-go-related gene (hERG).

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A novel least squares support vector machine ensemble ...

Downloadable (with restrictions)! Real operation data of power plants are inclined to be concentrated in some local areas because of the operators’ habits and control system design. In this paper, a novel least squares support vector machine (LSSVM)-based ensemble learning paradigm is proposed to predict NOx emission of a coal-fired boiler using real operation data.

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Novel machine learning ensemble approach for landslide ...

Mar 28, 2019  Ensemble approach based on Support Vector Machine (SVM), Naïve Bayes model were combined and implemented for the final prediction. This study possesses a strong capability to predict landslides by causative factors, slope, land use, elevation, geology, soil materials and triggering factor; rainfall was extracted and applied to the machine ...

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A Hybrid Support Vector Machine Ensemble Model for

model for credit scoring is designed which applies ensemble learning for credit granting decisions. The hybrid model combines clustering and classification techniques. Ten Support Vector Machine (SVM) classifiers are utilized as the members of ensemble model. Since even a small improvement in credit

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EnsembleSVM: A Library for Ensemble Learning Using

Journal of Machine Learning Research 15 (2014) 141-145 Submitted 4/13; Revised 10/13; Published 1/14 EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines Marc Claesen [email protected] KU Leuven, ESAT { STADIUS/iMinds Future Health Kasteelpark Arenberg 10, box 2446 3001 Leuven, Belgium Frank De Smet frank ...

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Transfer learning–based ensemble support vector machine ...

Mar 18, 2021  The novel discovered disease coronavirus popularly known as COVID-19 is caused due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and declared a pandemic by the World Health Organization (WHO). An early-stage detection of COVID-19 is crucial for the containment of the pandemic it has caused. In this study, a transfer learning–based COVID-19 screening technique is

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Flood susceptibility mapping using a novel ensemble ...

May 06, 2014  A novel ensemble SVM and WoE method was used for flood susceptibility mapping. ... Then, these factors were reclassified using the acquired weights and entered into the support vector machine (SVM) model to evaluate the correlation between flood occurrence and each conditioning factor. Through this integration, the weak point of WoE can be ...

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Transfer learning based ensemble support vector machine ...

Jun 03, 2020  Transfer learning based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data Mukul Singh 1 , Shrey Bansal 1 , Sakshi Ahuja 2 , Rahul Kumar Dubey 3 , Bijaya Ketan Panigrahi 2 , and Nilanjan Dey 4

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Ensemble Generalized Multiclass Support-Vector-Machine ...

Jul 15, 2020  Abstract: Accurate health evaluation is crucial to reliable operation of complex degradation systems. Although traditional machine learning methods such as artificial neural network (ANN) and support vector machine (SVM) have been used widely, state assessment schemes based on a single classification model still suffer from low multiclass classification efficiency, high variance, and deviation.

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Flood susceptibility analysis and its verification using a ...

Jan 11, 2015  The existing models for flood mapping have some weak points that may improve through more sophisticated and ensemble methods. The current research aimed to propose a novel ensemble method by integrating support vector machine (SVM) and frequency ratio (FR) to produce spatial modeling in flood susceptibility assessment.

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Support Vector Machine: Introduction - Analytics Vidhya

Jun 19, 2021  1. What is a Support Vector Machine? 2. What is the decision rule in SVM? 3. Determining the width of the margin boundaries. 4. Maximising the width. 5. Determining the hyperplane in N-dimensions. What is a Support Vector Machine? Support Vector Machine is a supervised learning algorithm that is used for both classification and regression analysis.

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A Novel Support Vector Machine Ensemble Based on ...

May 22, 2007  Abstract. This paper put forwards a novel support vector machine ensemble construction method based on subtractive clustering analysis. Firstly, the training samples are clustered into several clusters according to their distribution with subtractive clustering algorithm.

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A novel least squares support vector machine ensemble ...

Downloadable (with restrictions)! Real operation data of power plants are inclined to be concentrated in some local areas because of the operators’ habits and control system design. In this paper, a novel least squares support vector machine (LSSVM)-based ensemble learning paradigm is proposed to predict NOx emission of a coal-fired boiler using real operation data.

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Novel machine learning ensemble approach for landslide ...

Mar 28, 2019  Ensemble approach based on Support Vector Machine (SVM), Naïve Bayes model were combined and implemented for the final prediction. This study possesses a strong capability to predict landslides by causative factors, slope, land use, elevation, geology, soil materials and triggering factor; rainfall was extracted and applied to the machine ...

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A Hybrid Support Vector Machine Ensemble Model for

model for credit scoring is designed which applies ensemble learning for credit granting decisions. The hybrid model combines clustering and classification techniques. Ten Support Vector Machine (SVM) classifiers are utilized as the members of ensemble model. Since even a small improvement in credit

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Transfer learning-based ensemble support vector machine ...

The novel discovered disease coronavirus popularly known as COVID-19 is caused due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and declared a pandemic by the World Health Organization (WHO). ... TY - JOUR T1 - Transfer learning-based ensemble support vector machine model for automated COVID-19 detection using lung ...

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SSEM: A Novel Self-Adaptive Stacking Ensemble Model for ...

Aug 13, 2019  In the past decades, the ensemble systems have been shown as an efficient method to increase the accuracy and stability of classification algorithms. However, how to get a valid combination of multiple base-classifiers is still an open question to be solved. In this paper, based on the genetic algorithm, a new self-adaptive stacking ensemble model (called SSEM) is proposed. Different from ...

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Support Vector Machines for Machine Learning

Aug 15, 2020  Support Vector Machines are perhaps one of the most popular and talked about machine learning algorithms. They were extremely popular around the time they were developed in the 1990s and continue to be the go-to method for a high-performing algorithm with little tuning. In this post you will discover the Support Vector Machine (SVM) machine learning algorithm.

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Flood susceptibility mapping using a novel ensemble ...

Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS ABSTRACT Flood is one of the most devastating natural disasters that occur frequently in Terengganu, Malaysia. Recently, ensemble based techniques are

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A novel GIS-based ensemble technique for flood ...

Jun 02, 2019  In this study, we propose and test a novel ensemble method for improving the accuracy of each method in flood susceptibility mapping using evidential belief function (EBF) and support vector machine (SVM). The outcome of the proposed method was compared with the results of each method. The proposed method was implemented four times using different SVM kernels.

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A Novel Method for Mechanical Fault Diagnosis Based on ...

A novel fault diagnosis method based on variational mode decomposition (VMD) and multikernel support vector machine (MKSVM) optimized by Immune Genetic Algorithm (IGA) is proposed to accurately and adaptively diagnose mechanical faults. First, mechanical fault vibration signals are decomposed into multiple Intrinsic Mode Functions (IMFs) by VMD.

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Transfer learning based ensemble support vector machine ...

Jun 03, 2020  Transfer learning based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data Mukul Singh 1 , Shrey Bansal 1 , Sakshi Ahuja 2 , Rahul Kumar Dubey 3 , Bijaya Ketan Panigrahi 2 , and Nilanjan Dey 4

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Support vector machine — Machine learning book

Support-vector machine weights have also been used to interpret SVM models in the past. Posthoc interpretation of support-vector machine models in order to identify features used by the model to make predictions is a relatively new area of research with special significance in the biological sciences.

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A Novel Hybrid Model for Solar Radiation Forecasting Using ...

Aug 18, 2020  This article proposes a new hybrid least squares-support vector machine and artificial bee colony algorithm (ABC-LS-SVM) for multi-hour ahead forecasting of global solar radiation (GHI) data. The framework performs on training the least squares-support vector machine (LS-SVM) model by means of the ABC algorithm using the measured data.

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Flood susceptibility mapping using a novel ensemble ...

Flood is one of the most devastating natural disasters that occur frequently in Terengganu, Malaysia. Recently, ensemble based techniques are getting extremely popular in flood modeling. In this paper, weights-of-evidence (WoE) model was utilized first, to assess the impact of classes of each conditioning factor on flooding through bivariate statistical analysis (BSA).

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