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Drucker Diagnostics 1999 svm spam dataset
1999 Svm Spam Dataset, supplied by Drucker Diagnostics, 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/10__55041_slash_isjem06439-48-82-78
Average 86 stars, based on 1 article reviews
1999 svm spam dataset - by Bioz Stars, 2026-09
86/100 stars

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Article Title: Upscaling plot-scale soil respiration in winter wheat and summer maize rotation croplands in Julu County, North China
Article Snippet: Soil respiration (Rs) data from 45 plots were used to estimate the spatial patterns of Rs during the peak growing seasons of winter wheat and summer maize in Julu County, North China, by combining satellite remote sensing data, field-measured data, and a support vector regression (SVR) model.. The observed Rs values were well reproduced by the model at the plot scale, with a root-mean-square error (RMSE) of 0.31 mol CO2 m−2 s−1 and a coefficient of determination (R2) of 0.73.. No significant difference was detected between the prediction accuracy of the SVR model for winter wheat and summer maize.

Article Title: A new feature selection method for handling redundant information in text classification
Article Snippet: Feature selection is an important approach to dimensionality reduction in the field of text classification.. Because of the difficulty in handling the problem that the selected features always contain redundant information, we propose a new simple feature selection method, which can effectively filter the redundant features.. First, to calculate the relationship between two words, the definitions of word frequency based relevance and correlative redundancy are introduced.

Article Title: IMPROVED CONSTRUCTION SUBCONTRACTOR EVALUATION PERFORMANCE USING ESIM
Article Snippet: Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform.. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content.. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis.

Article Title: Insights Into Microseism Sources by Array and Machine Learning Techniques: Ionian and Tyrrhenian Sea Case of Study
Article Snippet: Finally, SVMs are supervised learning models for both classification and regression analysis (e.g., Drucker et al., 1997; Kuhn and Johnson, 2013).

Article Title: Support vector machines and generalized linear models for quantifying soil dehydrogenase activity in agro-forestry system of mid altitude central Himalaya
Article Snippet: In natural ecosystems, the linkages between inputs of carbon from plants, soil moisture (SM) and microbial activity are central to our understanding of nutrient cycling.. Predictions of microbial activities in soil are important as they indicate the potential of the soil to support biochemical processes that are essential for the maintenance of soil fertility as well as productivity.. The dehydrogenase activity (DHA) in soil provides information on microbial activities of the soil.



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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.

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: 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