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svm  (Lotte Chemical Corporation)

 
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    Lotte Chemical Corporation svm
    Svm, supplied by Lotte Chemical Corporation, 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/product/svms/svm/pm42162311-81-0-25
    Average 86 stars, based on 1 article reviews
    svm - by Bioz Stars, 2026-09
    86/100 stars

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    Article Title: Status of deep learning for EEG-based brain–computer interface applications
    Article Snippet: Artificial neural networks (ANNs), k-nearest neighbor (KNN), and SVMs are some of these machine learning approaches (Lotte et al., ; Akhter et al., ; Islam et al., ).

    Article Title: Long-Range α-Synchronization as Control Signal for BCI: A Feasibility Study.
    Article Snippet: Nonlinear SVMs are widely employed in decoding cognitive neural correlates of behavioral states (Lotte et al., 2007).

    Article Title: Paired Trial Classification: A Novel Deep Learning Technique for MVPA
    Article Snippet: Although an increasing number of studies are using deep learning to process neuroimaging datasets, the improvements in performance have typically not been as drastic as in other fields (Lotte et al., ), and most human neuroscience research has continued to use more traditional multivariate pattern analysis (MVPA) approaches: Manual feature extraction followed by a simple, typically linear, classifier, such as support vector machines (SVMs; Cortes and Vapnik, ) or logistic regression and its derivatives, e.g., sparse multinomial logistic regression (SMLR; Krishnapuram et al., ).

    Plasmid Preparation:

    Article Title: Status of deep learning for EEG-based brain–computer interface applications
    Article Snippet: Artificial neural networks (ANNs), k-nearest neighbor (KNN), and SVMs are some of these machine learning approaches (Lotte et al., ; Akhter et al., ; Islam et al., ).

    Article Title: Long-Range α-Synchronization as Control Signal for BCI: A Feasibility Study.
    Article Snippet: Nonlinear SVMs are widely employed in decoding cognitive neural correlates of behavioral states (Lotte et al., 2007).

    Article Title: Paired Trial Classification: A Novel Deep Learning Technique for MVPA
    Article Snippet: Although an increasing number of studies are using deep learning to process neuroimaging datasets, the improvements in performance have typically not been as drastic as in other fields (Lotte et al., ), and most human neuroscience research has continued to use more traditional multivariate pattern analysis (MVPA) approaches: Manual feature extraction followed by a simple, typically linear, classifier, such as support vector machines (SVMs; Cortes and Vapnik, ) or logistic regression and its derivatives, e.g., sparse multinomial logistic regression (SMLR; Krishnapuram et al., ).



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    Image Search Results


    Comparative biomarker performance in SVM classification. SVM models leveraging dynamic functional connectivity (dFC) demonstrated superior classification performance (C, F), outperforming models based on regional indices PerAF (A, D) and dALFF (B, E). For each biomarker, the grid‐search optimized parameters (top) and validation ROC curves (bottom) are shown, underscoring dFC as a highly discriminative feature for identifying disease‐specific neural signatures.

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    Article Title: Decoding Post‐Stroke Cognitive Impairment After Acute Basal Ganglia Infarction: The Synergistic Role of Functional Segregation and Integration in an SVM fMRI Framework

    doi: 10.1002/cns.70871

    Figure Lengend Snippet: Comparative biomarker performance in SVM classification. SVM models leveraging dynamic functional connectivity (dFC) demonstrated superior classification performance (C, F), outperforming models based on regional indices PerAF (A, D) and dALFF (B, E). For each biomarker, the grid‐search optimized parameters (top) and validation ROC curves (bottom) are shown, underscoring dFC as a highly discriminative feature for identifying disease‐specific neural signatures.

    Article Snippet: PSCI patients exhibit altered cerebellar‐cortical dynamics in PerAF, dALFF, and dFC, and an SVM classifier based on dFC features achieves 94.52% accuracy and 0.98 AUC, outperforming single‐metric models.

    Techniques: Biomarker Discovery, Functional Assay

    Enhanced diagnostic classification using combined biomarkers. Integration of multimodal neuroimaging metrics (PerAF, dALFF, dFC) yields a powerful classifier for PSCI. The SVM model, optimized via grid search (A), achieves superior discriminatory performance, as evidenced by the ROC curve in (B), outperforming models based on single metrics.

    Journal: CNS Neuroscience & Therapeutics

    Article Title: Decoding Post‐Stroke Cognitive Impairment After Acute Basal Ganglia Infarction: The Synergistic Role of Functional Segregation and Integration in an SVM fMRI Framework

    doi: 10.1002/cns.70871

    Figure Lengend Snippet: Enhanced diagnostic classification using combined biomarkers. Integration of multimodal neuroimaging metrics (PerAF, dALFF, dFC) yields a powerful classifier for PSCI. The SVM model, optimized via grid search (A), achieves superior discriminatory performance, as evidenced by the ROC curve in (B), outperforming models based on single metrics.

    Article Snippet: PSCI patients exhibit altered cerebellar‐cortical dynamics in PerAF, dALFF, and dFC, and an SVM classifier based on dFC features achieves 94.52% accuracy and 0.98 AUC, outperforming single‐metric models.

    Techniques: Diagnostic Assay