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matlab-based binary svm classifiers  (MathWorks Inc)


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    MathWorks Inc matlab-based binary svm classifiers
    Matlab Based Binary Svm 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
    https://www.bioz.com/product/binary+svm+classifier/pm32283387-184-2-1
    Average 90 stars, based on 1 article reviews
    matlab-based binary svm classifiers - by Bioz Stars, 2026-09
    90/100 stars

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    other:

    Article Title: Multi-modal knowledge base generation from very high resolution satellite imagery for habitat mapping
    Article Snippet: The SVM algorithm was a collection of multiple binary SVM classifiers and implemented in Matlab.

    Article Title: Motor imagery EEG classification based on ensemble support vector learning.
    Article Snippet: Background and Objective: Brain-computer interfaces build a communication pathway from the human brain to a computer.. Motor imagery-based electroencephalogram (EEG) classification is a widely applied paradigm in braincomputer interfaces.. The common spatial pattern, based on the event-related desynchronization (ERD)/event-related synchronization (ERS) phenomenon, is one of the most popular algorithms for motor imagery-based EEG classification.

    Article Title: Common and Distinct Roles of Frontal Midline Theta and Occipital Alpha Oscillations in Coding Temporal Intervals and Spatial Distances.
    Article Snippet: Binary SVM classifiers were implemented in MATLAB, with the function fitcsvm(), with the kernel function set up as linear.



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    System description.

    Journal: Biomedical Informatics Insights

    Article Title: Sentiment Analysis of Suicide Notes: A Shared Task

    doi: 10.4137/BII.S9042

    Figure Lengend Snippet: System description.

    Article Snippet: National Research Council Canada , NRC , Word unigrams and bigrams, thesaurus matches, character 4-grams, document length, various sentence-level patterns , None , 71061 , 608448/(71061 * 4633) = 0.00185 , Feature vectors normalized to unit length , Binary SVM; one-classifier-per-label , None , 10-fold cross validation , 0.5522.

    Techniques: Selection, Biomarker Discovery, Labeling, Expressing, Generated, Sequencing