k-nearest neighbor (knn) classification Search Results


k-nearest-neighbor imputation  (MathWorks Inc)
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k-nearest-neighbours (knn) average error and 3-prioritised variant  (MathWorks Inc)
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MathWorks Inc k-nearest-neighbours (knn) average error and 3-prioritised variant
K Nearest Neighbours (Knn) Average Error And 3 Prioritised Variant, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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k-nearest neighbors classifier  (MathWorks Inc)
90
MathWorks Inc k-nearest neighbors classifier
K Nearest Neighbors Classifier, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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k-nearest neighbor knn classification model  (MathWorks Inc)
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MathWorks Inc k-nearest neighbor knn classification model
K Nearest Neighbor Knn Classification Model, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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implementation of k-nearest neighbor classifier  (MathWorks Inc)
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MathWorks Inc implementation of k-nearest neighbor classifier
Implementation Of K Nearest Neighbor Classifier, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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knn algorithm  (MathWorks Inc)
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MathWorks Inc knn algorithm
Knn Algorithm, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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optimizable knn  (MathWorks Inc)
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MathWorks Inc optimizable knn
All tested classifiers ranked in terms of accuracy (%).
Optimizable Knn, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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k-nearest neighbors teknik informatika  (Teknik Hizmetler)
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Teknik Hizmetler k-nearest neighbors teknik informatika
All tested classifiers ranked in terms of accuracy (%).
K Nearest Neighbors Teknik Informatika, supplied by Teknik Hizmetler, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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k-nearest neighbor method  (Tetko Inc)
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All tested classifiers ranked in terms of accuracy (%).
K Nearest Neighbor Method, supplied by Tetko Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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svm learner  (KNIME GmbH)
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All tested classifiers ranked in terms of accuracy (%).
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k-nearest neighbor (knn) algorithm  (MathWorks Inc)
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All tested classifiers ranked in terms of accuracy (%).
K Nearest Neighbor (Knn) Algorithm, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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gradient boosted trees learner/predictor  (KNIME GmbH)
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KNIME GmbH gradient boosted trees learner/predictor
All tested classifiers ranked in terms of accuracy (%).
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Image Search Results


All tested classifiers ranked in terms of accuracy (%).

Journal: Journal of Clinical Medicine

Article Title: Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects

doi: 10.3390/jcm10225330

Figure Lengend Snippet: All tested classifiers ranked in terms of accuracy (%).

Article Snippet: In detail: Optimizable KNN (k-nearest neighbor classifier), via the “fitcknn” function ( https://it.mathworks.com/help/stats/fitcknn.html , accessed on 1 August 2021) with a total of 100 optimization iterations and no standardization of the input features; Optimizable SVM (support vector machine classifier), via the “fitcsvm” function ( https://it.mathworks.com/help/stats/fitcsvm.html , accessed on 1 August 2021) with a total of 100 optimization iterations and no standardization of the input features.

Techniques: Plasmid Preparation

Performance of the trained optimizable k-nearest neighbor (KNN) classifier. ( a ) Confusion matrix showing the true and predicted classes of the ToF patients’ videos. The blue diagonal is related to the number of videos that were truly recognized (Predicted Class = True Class), whereas the pink antidiagonal is related to the number of videos that were falsely recognized (Predicted Class ≠ True Class). ( b ) Same as ( a ) with the True Positive Rate (TPR) in blue and the False Negative Rate (FNR) in pink.

Journal: Journal of Clinical Medicine

Article Title: Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects

doi: 10.3390/jcm10225330

Figure Lengend Snippet: Performance of the trained optimizable k-nearest neighbor (KNN) classifier. ( a ) Confusion matrix showing the true and predicted classes of the ToF patients’ videos. The blue diagonal is related to the number of videos that were truly recognized (Predicted Class = True Class), whereas the pink antidiagonal is related to the number of videos that were falsely recognized (Predicted Class ≠ True Class). ( b ) Same as ( a ) with the True Positive Rate (TPR) in blue and the False Negative Rate (FNR) in pink.

Article Snippet: In detail: Optimizable KNN (k-nearest neighbor classifier), via the “fitcknn” function ( https://it.mathworks.com/help/stats/fitcknn.html , accessed on 1 August 2021) with a total of 100 optimization iterations and no standardization of the input features; Optimizable SVM (support vector machine classifier), via the “fitcsvm” function ( https://it.mathworks.com/help/stats/fitcsvm.html , accessed on 1 August 2021) with a total of 100 optimization iterations and no standardization of the input features.

Techniques:

Performance of the trained optimizable support vector machine (SVM) classifier. ( a ) Confusion matrix showing the true and predicted classes of the ToF patients’ videos. The blue diagonal is related to the number of videos that were truly recognized (Predicted Class = True Class), whereas the pink antidiagonal is related to the number of videos that were falsely recognized (Predicted Class ≠ True Class). ( b ) Same as ( a ) the True Positive Rate (TPR) in blue and the False Negative Rate (FNR) in pink.

Journal: Journal of Clinical Medicine

Article Title: Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects

doi: 10.3390/jcm10225330

Figure Lengend Snippet: Performance of the trained optimizable support vector machine (SVM) classifier. ( a ) Confusion matrix showing the true and predicted classes of the ToF patients’ videos. The blue diagonal is related to the number of videos that were truly recognized (Predicted Class = True Class), whereas the pink antidiagonal is related to the number of videos that were falsely recognized (Predicted Class ≠ True Class). ( b ) Same as ( a ) the True Positive Rate (TPR) in blue and the False Negative Rate (FNR) in pink.

Article Snippet: In detail: Optimizable KNN (k-nearest neighbor classifier), via the “fitcknn” function ( https://it.mathworks.com/help/stats/fitcknn.html , accessed on 1 August 2021) with a total of 100 optimization iterations and no standardization of the input features; Optimizable SVM (support vector machine classifier), via the “fitcsvm” function ( https://it.mathworks.com/help/stats/fitcsvm.html , accessed on 1 August 2021) with a total of 100 optimization iterations and no standardization of the input features.

Techniques: Plasmid Preparation

Optimization of the k-nearest neighbor (KNN) classifier. ( a ) The classifier was optimized over 100 iterations via the minimization of the classification error and its optimized hyperparameters are reported as “Optimization Results”. In detail, for each iteration of the optimization, the classifier was also 10-fold cross-validated. ( b ) Receiver Operating Characteristic (ROC) curve with the area under the curve (AUC) painted in blue; a value of AUC close to 1 means a very low classification error for the optimized classifier.

Journal: Journal of Clinical Medicine

Article Title: Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects

doi: 10.3390/jcm10225330

Figure Lengend Snippet: Optimization of the k-nearest neighbor (KNN) classifier. ( a ) The classifier was optimized over 100 iterations via the minimization of the classification error and its optimized hyperparameters are reported as “Optimization Results”. In detail, for each iteration of the optimization, the classifier was also 10-fold cross-validated. ( b ) Receiver Operating Characteristic (ROC) curve with the area under the curve (AUC) painted in blue; a value of AUC close to 1 means a very low classification error for the optimized classifier.

Article Snippet: In detail: Optimizable KNN (k-nearest neighbor classifier), via the “fitcknn” function ( https://it.mathworks.com/help/stats/fitcknn.html , accessed on 1 August 2021) with a total of 100 optimization iterations and no standardization of the input features; Optimizable SVM (support vector machine classifier), via the “fitcsvm” function ( https://it.mathworks.com/help/stats/fitcsvm.html , accessed on 1 August 2021) with a total of 100 optimization iterations and no standardization of the input features.

Techniques: