k-nn svm classification routines Search Results


selection operator xgboost extreme gradient boosting svm support vector machine ann artificial neural network knn k  (Genovis Inc)
93
Genovis Inc selection operator xgboost extreme gradient boosting svm support vector machine ann artificial neural network knn k
Selection Operator Xgboost Extreme Gradient Boosting Svm Support Vector Machine Ann Artificial Neural Network Knn K, supplied by Genovis Inc, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/selection operator xgboost extreme gradient boosting svm support vector machine ann artificial neural network knn k/product/Genovis Inc
Average 93 stars, based on 1 article reviews
selection operator xgboost extreme gradient boosting svm support vector machine ann artificial neural network knn k - by Bioz Stars, 2026-06
93/100 stars
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sony 3-cdd camera  (Sony)
90
Sony sony 3-cdd camera
Tongue classification and diseases.
Sony 3 Cdd Camera, supplied by Sony, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/sony 3-cdd camera/product/Sony
Average 90 stars, based on 1 article reviews
sony 3-cdd camera - by Bioz Stars, 2026-06
90/100 stars
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ensemble (knn-svm-rf)  (ASHRAE Inc)
90
ASHRAE Inc ensemble (knn-svm-rf)
Tongue classification and diseases.
Ensemble (Knn Svm Rf), supplied by ASHRAE Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/ensemble (knn-svm-rf)/product/ASHRAE Inc
Average 90 stars, based on 1 article reviews
ensemble (knn-svm-rf) - by Bioz Stars, 2026-06
90/100 stars
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machine leaning toolbox  (MathWorks Inc)
96
MathWorks Inc machine leaning toolbox
Tongue classification and diseases.
Machine Leaning Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/machine leaning toolbox/product/MathWorks Inc
Average 96 stars, based on 1 article reviews
machine leaning toolbox - by Bioz Stars, 2026-06
96/100 stars
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perbandingan metode knn dan svm dalam klasifikasi kematangan buah mangga berdasarkan citra hsv dan fitur statistik  (Statistik Georg Ferber)
86
Statistik Georg Ferber perbandingan metode knn dan svm dalam klasifikasi kematangan buah mangga berdasarkan citra hsv dan fitur statistik
Tongue classification and diseases.
Perbandingan Metode Knn Dan Svm Dalam Klasifikasi Kematangan Buah Mangga Berdasarkan Citra Hsv Dan Fitur Statistik, supplied by Statistik Georg Ferber, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/perbandingan metode knn dan svm dalam klasifikasi kematangan buah mangga berdasarkan citra hsv dan fitur statistik/product/Statistik Georg Ferber
Average 86 stars, based on 1 article reviews
perbandingan metode knn dan svm dalam klasifikasi kematangan buah mangga berdasarkan citra hsv dan fitur statistik - by Bioz Stars, 2026-06
86/100 stars
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tl vgg16-softmax  (SoftMax Inc)
90
SoftMax Inc tl vgg16-softmax
Model Overview: Comprehensive summary of the DL architectures employed across the reviewed papers. The table outlines key information, including the brain tumor classification task, data partitioning, architecture, and the reported performance metrics.
Tl Vgg16 Softmax, supplied by SoftMax Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/tl vgg16-softmax/product/SoftMax Inc
Average 90 stars, based on 1 article reviews
tl vgg16-softmax - by Bioz Stars, 2026-06
90/100 stars
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chi-square test  (CH Instruments)
90
CH Instruments chi-square test
Model Overview: Comprehensive summary of the DL architectures employed across the reviewed papers. The table outlines key information, including the brain tumor classification task, data partitioning, architecture, and the reported performance metrics.
Chi Square Test, supplied by CH Instruments, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/chi-square test/product/CH Instruments
Average 90 stars, based on 1 article reviews
chi-square test - by Bioz Stars, 2026-06
90/100 stars
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knn  (StatLog Inc)
90
StatLog Inc knn
Comparison of the related literature on the different datasets.
Knn, supplied by StatLog Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/knn/product/StatLog Inc
Average 90 stars, based on 1 article reviews
knn - by Bioz Stars, 2026-06
90/100 stars
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svm  (StatLog Inc)
90
StatLog Inc svm
Comparison of the related literature on the different datasets.
Svm, supplied by StatLog Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/svm/product/StatLog Inc
Average 90 stars, based on 1 article reviews
svm - by Bioz Stars, 2026-06
90/100 stars
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svm knn  (Reddit Inc)
86
Reddit Inc svm knn
Comparison of the related literature on the different datasets.
Svm Knn, supplied by Reddit Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/svm knn/product/Reddit Inc
Average 86 stars, based on 1 article reviews
svm knn - by Bioz Stars, 2026-06
86/100 stars
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methods inf med 4/2012  (Schattauer GmbH)
90
Schattauer GmbH methods inf med 4/2012
Comparison of the related literature on the different datasets.
Methods Inf Med 4/2012, supplied by Schattauer GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/methods inf med 4/2012/product/Schattauer GmbH
Average 90 stars, based on 1 article reviews
methods inf med 4/2012 - by Bioz Stars, 2026-06
90/100 stars
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Image Search Results


Tongue classification and diseases.

Journal: Medical Review

Article Title: Digital tongue image analyses for health assessment

doi: 10.1515/mr-2021-0018

Figure Lengend Snippet: Tongue classification and diseases.

Article Snippet: Ding [ ] , 2016 , Gastritis , KNN, RF, SVM , Textural , 326 , SONY 3-CDD camera , 89.10%.

Techniques: Diagnostic Assay

Model Overview: Comprehensive summary of the DL architectures employed across the reviewed papers. The table outlines key information, including the brain tumor classification task, data partitioning, architecture, and the reported performance metrics.

Journal: Cancers

Article Title: Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology

doi: 10.3390/cancers16020300

Figure Lengend Snippet: Model Overview: Comprehensive summary of the DL architectures employed across the reviewed papers. The table outlines key information, including the brain tumor classification task, data partitioning, architecture, and the reported performance metrics.

Article Snippet: 100 , Rajinikanth et al. [ ] (2022) , LGG vs. HGG , 5-fold CV , 90:10 , - , TL VGG16-SoftMax TL VGG16-DT TL VGG16-KNN TL VGG16-SVM , 96.50 96.00 96.50 97.00 , - - - - , 96.55 96.00 96.52 97.00 , (R) 97.03, (S) 95.96 (R) 96.97, (S) 95.05 (R) 97.00, (S) 96.00 (R) 97.00, (S) 97.00.

Techniques: Blocking Assay

Comparison of the related literature on the different datasets.

Journal: PeerJ Computer Science

Article Title: Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

doi: 10.7717/peerj-cs.1917

Figure Lengend Snippet: Comparison of the related literature on the different datasets.

Article Snippet: Cleveland And Statlog , , KNN, SVM, RF, NB and NN , Using 6 features KNN 86%, SVM 83%, RF 91%, NB 87% and NN 86% , 2019.

Techniques: Comparison, Selection

Comparison of the related literature based on different datasets.

Journal: PeerJ Computer Science

Article Title: Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

doi: 10.7717/peerj-cs.1917

Figure Lengend Snippet: Comparison of the related literature based on different datasets.

Article Snippet: Cleveland And Statlog , , KNN, SVM, RF, NB and NN , Using 6 features KNN 86%, SVM 83%, RF 91%, NB 87% and NN 86% , 2019.

Techniques: Comparison

Hyperparameters tuning of the classifiers using grid search CV.

Journal: PeerJ Computer Science

Article Title: Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

doi: 10.7717/peerj-cs.1917

Figure Lengend Snippet: Hyperparameters tuning of the classifiers using grid search CV.

Article Snippet: Cleveland And Statlog , , KNN, SVM, RF, NB and NN , Using 6 features KNN 86%, SVM 83%, RF 91%, NB 87% and NN 86% , 2019.

Techniques: Activation Assay

Performance of the classifiers on the single and the combined dataset without applying the proposed preprocessing pipeline.

Journal: PeerJ Computer Science

Article Title: Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

doi: 10.7717/peerj-cs.1917

Figure Lengend Snippet: Performance of the classifiers on the single and the combined dataset without applying the proposed preprocessing pipeline.

Article Snippet: Cleveland And Statlog , , KNN, SVM, RF, NB and NN , Using 6 features KNN 86%, SVM 83%, RF 91%, NB 87% and NN 86% , 2019.

Techniques:

Performance of the classifiers on different combinations of the datasets without applying the proposed pipeline.

Journal: PeerJ Computer Science

Article Title: Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

doi: 10.7717/peerj-cs.1917

Figure Lengend Snippet: Performance of the classifiers on different combinations of the datasets without applying the proposed pipeline.

Article Snippet: Cleveland And Statlog , , KNN, SVM, RF, NB and NN , Using 6 features KNN 86%, SVM 83%, RF 91%, NB 87% and NN 86% , 2019.

Techniques:

Performance of the classifiers on the single and the combined dataset applying PCA on the proposed pipeline.

Journal: PeerJ Computer Science

Article Title: Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

doi: 10.7717/peerj-cs.1917

Figure Lengend Snippet: Performance of the classifiers on the single and the combined dataset applying PCA on the proposed pipeline.

Article Snippet: Cleveland And Statlog , , KNN, SVM, RF, NB and NN , Using 6 features KNN 86%, SVM 83%, RF 91%, NB 87% and NN 86% , 2019.

Techniques:

Performance of the classifiers on different combinations of the datasets applying PCA on the proposed pipeline.

Journal: PeerJ Computer Science

Article Title: Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

doi: 10.7717/peerj-cs.1917

Figure Lengend Snippet: Performance of the classifiers on different combinations of the datasets applying PCA on the proposed pipeline.

Article Snippet: Cleveland And Statlog , , KNN, SVM, RF, NB and NN , Using 6 features KNN 86%, SVM 83%, RF 91%, NB 87% and NN 86% , 2019.

Techniques:

Performance of the classifiers on the single and the combined dataset applying RF on the proposed pipeline.

Journal: PeerJ Computer Science

Article Title: Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

doi: 10.7717/peerj-cs.1917

Figure Lengend Snippet: Performance of the classifiers on the single and the combined dataset applying RF on the proposed pipeline.

Article Snippet: Cleveland And Statlog , , KNN, SVM, RF, NB and NN , Using 6 features KNN 86%, SVM 83%, RF 91%, NB 87% and NN 86% , 2019.

Techniques:

Performance of the classifiers on different combinations of the datasets applying RF.

Journal: PeerJ Computer Science

Article Title: Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

doi: 10.7717/peerj-cs.1917

Figure Lengend Snippet: Performance of the classifiers on different combinations of the datasets applying RF.

Article Snippet: Cleveland And Statlog , , KNN, SVM, RF, NB and NN , Using 6 features KNN 86%, SVM 83%, RF 91%, NB 87% and NN 86% , 2019.

Techniques:

Comparison of the related literature on the different datasets.

Journal: PeerJ Computer Science

Article Title: Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

doi: 10.7717/peerj-cs.1917

Figure Lengend Snippet: Comparison of the related literature on the different datasets.

Article Snippet: Statlog, Cleveland , , SVM, LR, DNN, DT, NB, RF, KNN , Best accuracy SVM (97% fivefold) , 2020.

Techniques: Comparison, Selection

Comparison of the related literature based on different datasets.

Journal: PeerJ Computer Science

Article Title: Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

doi: 10.7717/peerj-cs.1917

Figure Lengend Snippet: Comparison of the related literature based on different datasets.

Article Snippet: Statlog, Cleveland , , SVM, LR, DNN, DT, NB, RF, KNN , Best accuracy SVM (97% fivefold) , 2020.

Techniques: Comparison

Hyperparameters tuning of the classifiers using grid search CV.

Journal: PeerJ Computer Science

Article Title: Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

doi: 10.7717/peerj-cs.1917

Figure Lengend Snippet: Hyperparameters tuning of the classifiers using grid search CV.

Article Snippet: Statlog, Cleveland , , SVM, LR, DNN, DT, NB, RF, KNN , Best accuracy SVM (97% fivefold) , 2020.

Techniques: Activation Assay

Performance of the classifiers on the single and the combined dataset without applying the proposed preprocessing pipeline.

Journal: PeerJ Computer Science

Article Title: Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

doi: 10.7717/peerj-cs.1917

Figure Lengend Snippet: Performance of the classifiers on the single and the combined dataset without applying the proposed preprocessing pipeline.

Article Snippet: Statlog, Cleveland , , SVM, LR, DNN, DT, NB, RF, KNN , Best accuracy SVM (97% fivefold) , 2020.

Techniques:

Performance of the classifiers on different combinations of the datasets without applying the proposed pipeline.

Journal: PeerJ Computer Science

Article Title: Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

doi: 10.7717/peerj-cs.1917

Figure Lengend Snippet: Performance of the classifiers on different combinations of the datasets without applying the proposed pipeline.

Article Snippet: Statlog, Cleveland , , SVM, LR, DNN, DT, NB, RF, KNN , Best accuracy SVM (97% fivefold) , 2020.

Techniques:

Performance of the classifiers on the single and the combined dataset applying PCA on the proposed pipeline.

Journal: PeerJ Computer Science

Article Title: Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

doi: 10.7717/peerj-cs.1917

Figure Lengend Snippet: Performance of the classifiers on the single and the combined dataset applying PCA on the proposed pipeline.

Article Snippet: Statlog, Cleveland , , SVM, LR, DNN, DT, NB, RF, KNN , Best accuracy SVM (97% fivefold) , 2020.

Techniques:

Performance of the classifiers on different combinations of the datasets applying PCA on the proposed pipeline.

Journal: PeerJ Computer Science

Article Title: Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

doi: 10.7717/peerj-cs.1917

Figure Lengend Snippet: Performance of the classifiers on different combinations of the datasets applying PCA on the proposed pipeline.

Article Snippet: Statlog, Cleveland , , SVM, LR, DNN, DT, NB, RF, KNN , Best accuracy SVM (97% fivefold) , 2020.

Techniques:

Performance of the classifiers on the single and the combined dataset applying RF on the proposed pipeline.

Journal: PeerJ Computer Science

Article Title: Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

doi: 10.7717/peerj-cs.1917

Figure Lengend Snippet: Performance of the classifiers on the single and the combined dataset applying RF on the proposed pipeline.

Article Snippet: Statlog, Cleveland , , SVM, LR, DNN, DT, NB, RF, KNN , Best accuracy SVM (97% fivefold) , 2020.

Techniques:

Performance of the classifiers on different combinations of the datasets applying RF.

Journal: PeerJ Computer Science

Article Title: Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets

doi: 10.7717/peerj-cs.1917

Figure Lengend Snippet: Performance of the classifiers on different combinations of the datasets applying RF.

Article Snippet: Statlog, Cleveland , , SVM, LR, DNN, DT, NB, RF, KNN , Best accuracy SVM (97% fivefold) , 2020.

Techniques: