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classification learner packages  (MathWorks Inc)


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    Structured Review

    MathWorks Inc classification learner packages
    Classification Learner Packages, 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/product/classification-learner+package/pmc05394550-288-8-12
    Average 90 stars, based on 1 article reviews
    classification learner packages - by Bioz Stars, 2026-09
    90/100 stars

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    Related Articles

    Control:

    Article Title: Intelligent Fault-Tolerant Control of Delta Robots: A Hybrid Optimization Approach for Enhanced Trajectory Tracking.
    Article Snippet: .. Control signals are stored, and based on these data, features are extracted and used in MATLAB’s Classification Learner to detect and diagnose the fault state of the manipulator. ..

    Comparison:

    Article Title: Assessment of soil classification based on cone penetration test data for Kaifeng area using optimized support vector machine
    Article Snippet: .. Consequently, it is proposed to enhance the classification performance by integrating optimization algorithms Based on a comparison conducted using the training dataset through the ’Classification Learner’ app in Matlab R2023a, it was found that SVM demonstrated higher accuracy than other models. ..

    other:

    Article Title: Machine learning and chemometric methods for high-throughput authentication of 53 Root and Rhizome Chinese Herbal using ATR-FTIR fingerprints.
    Article Snippet: To address the identification challenges caused by morphological similarities in Root and Rhizome Chinese Herbal (RRCH), this study developed a discrimination system integrating Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR) with multimodal machine learning.. 53 kinds of RRCH collected from China were analyzed using ATR-FTIR to acquire spectral fingerprints.. An innovative analytical framework was established, combining chemometric Partial Least Squares Discriminant Analysis (PLS-DA) with optimized machine learning models: t-distributed Stochastic Neighbor Embedding (t-SNE), optimized decision trees, optimized discriminant analysis, naive Bayes, optimized SVM, optimized KNN, SVM kernels, and optimized ensemble learning.

    Article Title: Toward handheld optically guided biopsy combining diffuse reflectance spectroscopy and autofluorescence
    Article Snippet: A supervised machine learning model was used for the data analysis using MATLAB’s Classification Learner (MathWorks Inc., Natick, Massachusetts, United States).

    Generated:

    Article Title: Advancing Pulmonary Embolism Detection with Integrated Deep Learning Architectures.
    Article Snippet: The main aim of this study is to introduce a new hybrid deep learning model for biomedical image classification.. We propose a novel convolutional neural network (CNN), named HybridNeXt, for detecting pulmonary embolism (PE) from computed tomography (CT) images.. To evaluate the HybridNeXt model, we created a new dataset consisting of two classes: (1) PE and (2) control.



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    MathWorks Inc classification-learner package
    Characteristics, classification and objectives of the machine learning models used in the studies
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    Characteristics, classification and objectives of the machine learning models used in the studies
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    Characteristics, classification and objectives of the machine learning models used in the studies
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    MathWorks Inc classification learner package in
    Characteristics, classification and objectives of the machine learning models used in the studies
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    MathWorks Inc classification learner packages
    Characteristics, classification and objectives of the machine learning models used in the studies
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    https://www.bioz.com/product/classification-learner+package/pmc05394550-288-8-12
    Average 90 stars, based on 1 article reviews
    classification learner packages - by Bioz Stars, 2026-09
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      Buy from Supplier

    Image Search Results


    Characteristics, classification and objectives of the machine learning models used in the studies

    Journal: BMC Medical Research Methodology

    Article Title: Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review

    doi: 10.1186/s12874-025-02463-y

    Figure Lengend Snippet: Characteristics, classification and objectives of the machine learning models used in the studies

    Article Snippet: Varghese [ ] b , Incidence risk stratification (classification): QSVM (radiomics-based) , Risk stratification for prostate cancer in low- and high-risk patients , desktop-based , CDSS , MATLAB's Classification-Learner Package , NR , NR.

    Techniques: Software, Biomarker Discovery, Diagnostic Assay, Imaging, Staining