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25 results for “Support Vector Machines”
Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European Marine Species, developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Native ecological niche models of 1508 European species (894 fish and 614 non fish) developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 and under RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, at 0.5° spatial resolution.</p>
Biodiversity Index, in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European Marine Species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Biodiversity Index in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European marine species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution. The Index counts the number of species (among the 1508) potentially present in each 0.5° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</p>
Ecological Niche Models of 96 European Marine Species, for 2019, developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines at 0.1° Resolution
<p>Native ecological niche models of 96 European marine species of particular commercial and conservation interest developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 at 0.1° spatial resolution.</p>
Ensemble Ecological Niche Models and Biodiversity Index for 2019 of 96 European Marine Species based on Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, AquaMaps, and Support Vector Machines at 0.1° Resolution
<p>Ensemble Ecological Niche Models for 2019 of 96 European marine species of particular commercial and conservation interest, based on Ecological Niche Models developed with (i) Artificial Neural Networks, (ii) Maximum Entropy, (iii) Support Vector Machines, and (iv) AquaMaps at 0.1° Resolution. The data report, for each 0.1° cell, how many models (from 0 to 4) overcome a model-specific decision threshold to assess species presence in the cell. A Biodiversity Index is also provided as the count of the number of species (among the 96) potentially present in each 0.1° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</p>
Sentiment Analysis of RUU PDP with Naive Bayes, Support Vector Machine, and Random Forest Classification Algorithm
<p>Dataset from the results of data crawling via Twitter which discusses the Rancangan Undang Undang Pelindungan Data Pribadi to be used in the sentiment analysis process. The dataset is divided into several parts according to the process executed on RapidMiner.</p>
Figure 4. A graphic on values of TP, TN, FP, and FN for each different application process.-Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes
<p>This study has proposed a diabetes diagnosis system, which is formed via both Support Vector Machines (SVM) and Cognitive Development Optimization Algorithm (CoDOA). In this approach, the training process of the SVM has been supported with the CoDOA and after determining the most optimum sigma (σ) parameter of the Gauss (RBF) kernel function (so the most optimum SVM), a better classification formation has been tried to be achieved. In the context of the study, diabetes data set, which is related to Pima Indians, has been used for evaluating effectiveness of the proposed approach and after six different application processes, it was seen that the approach is well-enough on classification, which means being capable of determining diabetes. There are also some future works regarding the developed CoDOA-SVM based approach. In this context, there will be some more works for improving classification accuracy and also setting different optimization plans on i.e. different parameters of the kernel function. Additionally, it is aimed to evaluate the approach with datasets belonging to different diseases.</p>
Figure 3. A brief schema of the CoDOA-SVM approach-Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes
<p>In the Equation 23, TP stands for true classified diabetes positive individuals; TN stands for true classified diabetes negative individuals; FP stands for false classified diabetes positive individuals and finally, FN stands for false classified diabetes negative individuals. • After determining good (optimum) particles, default CoDOA steps are run. • After achieving the total iteration number, it is allowed to train the SVM via optimum Gauss (RBF) kernel function parameters, by using the optimum particle value [sigma (σ) value]. • The trained SVM is now ready for the classification and so is diabetes determination process.<br> A brief schema of the CoDOA-SVM approach is also provided in Figure 3.</p>
Figure 1. Maximum margin hyper-plane (Cortes & Vapnik, 1995).-Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes
<p>In other words, it aims to find the state in which the distance between the two classes is the maximum. The hallmarks of this classification reasoning are the support vectors chosen from the training set, and they are located on the closest points of both classes (Javed, Ayyaz, & Mehmood, 2007). In Figure 1, an example of support vectors and a maximum margin hyper-plane (in other words, an optimum separating hyper- plane) is shown (Cortes & Vapnik, 1995).</p>
Fig. 1 in Classificação Digital de Cicadidae com Wavelets e Support Vector Machines
Fig. 1. Ninfa móvel de Quesada gigas
Fig. 2 in Classificação Digital de Cicadidae com Wavelets e Support Vector Machines
Fig. 2. Cigarras da espécie Quesada gigas, envolvidas nos experimentos
RDF Dataset for article: A confidence predictor for logD using conformal regression and a support-vector machine
<p>RDF dataset described in article: "A confidence predictor for logD using conformal regression and a support-vector machine" (Manuscript in preparation).</p> <p>The dataset contains conformal logD values at 90% confidence level, computed for 91M compounds from PubChem, in RDF format.</p> <p>The .hdt.gz version contains the dataset in RDF HDT format (http://www.rdfhdt.org/), compressed with tar and gzip. The archive contains both the .hdt file, and an index file, generated by the hdtSearch C++ tool.</p> <p>The .ttl.gz file is a gzipped file in RDF Turtle format (https://www.w3.org/TR/turtle/).</p>
Figure 2. Flow chart of the CoDOA (Kose & Arslan, 2015).0Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes
<p>The related algorithm steps can be visualized with a flow chart as shown in Figure 2 [26].</p>
An Empirical Comparative Study of Convolutional Neural Network and Support Vector Machine in Digital Signature for Digital Document Authentication
<p>Dataset dan figure of the research</p>
Dynamic implicit modeling of tunnel unfavorable geology based on multi-source data fusion using support vector machine
<p>This is the relevant data of the article "Dynamic implicit modeling of tunnel unfavorable geology based on multi-source data fusion using support vector machine"</p>
Compound data sets for support vector machine and regression modeling
<p>Provided are compound data sets used for support vector machine and support vector regression modeling and associated information.</p>
Datasets used in the papers: STree: A Single Multi-class Oblique Decision Tree Based on Support Vector Machines & ODTE - An ensemble of multi-class SVM-based oblique decision trees
<p>These are the 49 datasets used in the benchmark. 45 of them are from the UCI machine learning repository, while the other 4 correspond to a problem about fecundity estimation for fisheries</p>
Ensemble Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European Marine Species based on Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Ensemble Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European marine species based on Ecological Niche Models developed with (i) Artificial Neural Networks, (ii) Maximum Entropy, (iii) Support Vector Machines, and (iv) AquaMaps at 0.5° Resolution. The data report, for each 0.5° cell, how many models (from 0 to 4) overcome a model-specific decision threshold to assess species presence in the cell.</p>
Data from: Phylogeography and support vector machine classification of colour variation in panther chameleons
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Moderate-severe OSA screening based on support vector machine of the Chinese population facio-cervical measurements dataset: A cross-sectional study
<p><strong>Objectives</strong> Obstructive sleep apnea (OSA) has received much attention as a risk factor for perioperative complications and 68.5% of OSA patients remain undiagnosed before surgery. Facio-cervical characteristics may screen OSA for Asians due to smaller upper airways compared to Caucasians. Thus, our study aimed to explore a machine-learning model to screen moderate-severe OSA based on facio-cervical and anthropometric measurements.</p> <p><strong>Design </strong>A cross-sectional study.</p> <p><strong>Setting</strong> Data were collected from the Shanghai Jiao Tong University School of Medicine affiliated Ruijin Hospital between February 2019 and August 2020.</p> <p><strong>Participants</strong> A total of 481 Chinese participants were included in the study.</p> <p><strong>Primary and secondary outcome</strong> (1) Identification of moderate-severe OSA with apnea-hypopnea index (AHI)15 events·h<sup>−1</sup>. (2) Verification of the machine learning model.</p> <p><strong>Results</strong> The SABIHC2 model (Sex-Age-Body mass index-maximum Interincisal distance-ratio of Height to thyro-sternum distance-neck Circumference-waist Circumference) was set up. The SABIHC2 model could screen moderate-severe OSA with an area under the curve (AUC)=0.832, the sensitivity of 0.916, and specificity of 0.749, and performed better than the STOP-BANG questionnaire, which showed AUC=0.631, the sensitivity of 0.487, and specificity of 0.772. Especially for asymptomatic patients (ESS < 10), the SABIHC2 model demonstrated better predictive ability compared to the STOP-BANG questionnaire, with AUC (0.824 vs. 0.530), sensitivity (0.892 vs. 0.348), and specificity (0.755 vs. 0.809).</p> <p><strong>Conclusion</strong> The SABIHC2 machine learning model provides a simple and accurate assessment of moderate-severe OSA in the Chinese population, especially for those without significant daytime sleepiness.</p>
Data from: Pain intensity recognition rates via biopotential feature patterns with support vector machines
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.