svm classification matlab statistics and machine learning toolbox Search Results


fitcsvm  (MathWorks Inc)
90
MathWorks Inc fitcsvm
Fitcsvm, 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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machine learning package  (MathWorks Inc)
90
MathWorks Inc machine learning package
Machine Learning Package, 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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svm model  (MathWorks Inc)
90
MathWorks Inc svm model
Svm 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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svm model - by Bioz Stars, 2026-04
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linear svm classifier  (MathWorks Inc)
90
MathWorks Inc linear svm classifier
Linear Svm 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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matlab 2019a  (MathWorks Inc)
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MathWorks Inc matlab 2019a
Matlab 2019a, 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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classification learner r2018b  (MathWorks Inc)
90
MathWorks Inc classification learner r2018b
Classification Learner R2018b, 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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non-linear classifier nearest-neighbor model  (MathWorks Inc)
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MathWorks Inc non-linear classifier nearest-neighbor model
Non Linear Classifier Nearest Neighbor 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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multilayer artificial neural network classifiers  (MathWorks Inc)
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MathWorks Inc multilayer artificial neural network classifiers
The <t>nnet</t> architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.
Multilayer Artificial Neural Network Classifiers, 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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multilayer artificial neural network classifiers - by Bioz Stars, 2026-04
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learning algorithm (svm) implemented in the machine learning matlab toolbox  (MathWorks Inc)
90
MathWorks Inc learning algorithm (svm) implemented in the machine learning matlab toolbox
The <t>nnet</t> architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.
Learning Algorithm (Svm) Implemented In The Machine Learning Matlab Toolbox, 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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7.5b  (MathWorks Inc)
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MathWorks Inc 7.5b
The <t>nnet</t> architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.
7.5b, 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
https://www.bioz.com/result/7.5b/product/MathWorks Inc
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svm function 'fitcecoc  (MathWorks Inc)
90
MathWorks Inc svm function 'fitcecoc
The <t>nnet</t> architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.
Svm Function 'fitcecoc, 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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svm function 'fitcecoc - by Bioz Stars, 2026-04
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support vector machine svm classifier  (MathWorks Inc)
96
MathWorks Inc support vector machine svm classifier
The <t>nnet</t> architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.
Support Vector Machine Svm Classifier, 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
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Image Search Results


The nnet architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.

Journal: Frontiers in Oncology

Article Title: Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach

doi: 10.3389/fonc.2020.00593

Figure Lengend Snippet: The nnet architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.

Article Snippet: For the SCLC/NSCLC classification (a typical 2-class problem) from high-dimensional features in a number of tens to thousands as in our study, we used multilayer artificial neural network classifiers ( https://www.mathworks.com/help/stats/machine-learning-in-matlab.html ), which in principle could achieve more optimal arbitrary non-linear mapping (e.g., non-linearity beyond analytic description or mathematical tracking) with appropriate configuration and training.

Techniques:

Two scenarios for demonstrating the nnet “training-validating-testing” performance. Upper: one case of 1 misclassification; lower: one case of no misclassification. The panels designated as a1 and a2 present the nnet training behaviors under random initial settings (w: weight and b: bias); The panels designated as b1 and b2 present the output node values (in value range [−1,1], in black dots) in reference to target setting (SCLC = 1, NSCLC = -1); and the panels designated as c1 and c2 present the confusion matrices. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.

Journal: Frontiers in Oncology

Article Title: Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach

doi: 10.3389/fonc.2020.00593

Figure Lengend Snippet: Two scenarios for demonstrating the nnet “training-validating-testing” performance. Upper: one case of 1 misclassification; lower: one case of no misclassification. The panels designated as a1 and a2 present the nnet training behaviors under random initial settings (w: weight and b: bias); The panels designated as b1 and b2 present the output node values (in value range [−1,1], in black dots) in reference to target setting (SCLC = 1, NSCLC = -1); and the panels designated as c1 and c2 present the confusion matrices. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.

Article Snippet: For the SCLC/NSCLC classification (a typical 2-class problem) from high-dimensional features in a number of tens to thousands as in our study, we used multilayer artificial neural network classifiers ( https://www.mathworks.com/help/stats/machine-learning-in-matlab.html ), which in principle could achieve more optimal arbitrary non-linear mapping (e.g., non-linearity beyond analytic description or mathematical tracking) with appropriate configuration and training.

Techniques: