AIMC Topic: Support Vector Machine

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A Diadochokinesis-based expert system considering articulatory features of plosive consonants for early detection of Parkinson's disease.

Computer methods and programs in biomedicine Nov 16, 2017
BACKGROUND AND OBJECTIVE: A new expert system is proposed to discriminate healthy people from people with Parkinson's Disease (PD) in early stages by using Diadochokinesis tests.
Support Vector Machine Humans Parkinson Disease Algorithms Expert Systems Early Diagnosis Female Speech Acoustics Male Voice Reproducibility of Results Middle Aged Aged Case-Control Studies Aged, 80 and over
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A regularization path algorithm for support vector ordinal regression.

Neural networks : the official journal of the International Neural Network Society Nov 16, 2017
Support vector ordinal regression (SVOR) is a popular method for tackling ordinal regression problems. Solution path provides a compact representation of optimal solutions for all values of regularization parameter, which is extremely useful for mode...
Algorithms Support Vector Machine Datasets as Topic
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Support vector machine with Dirichlet feature mapping.

Neural networks : the official journal of the International Neural Network Society Nov 16, 2017
The Support Vector Machine (SVM) is a supervised learning algorithm to analyze data and recognize patterns. The standard SVM suffers from some limitations in nonlinear classification problems. To tackle these limitations, the nonlinear form of the SV...
Support Vector Machine Pattern Recognition, Automated
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Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets.

PloS one Nov 14, 2017
The prediction of the trends of stocks and index prices is one of the important issues to market participants. Investors have set trading or fiscal strategies based on the trends, and considerable research in various academic fields has been studied ...
Support Vector Machine Machine Learning Models, Economic Forecasting Commerce Republic of Korea
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Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma.

European radiology Nov 13, 2017
OBJECTIVE: To evaluate the diagnostic performance of machine-learning based quantitative texture analysis of CT images to differentiate small (≤ 4 cm) angiomyolipoma without visible fat (AMLwvf) from renal cell carcinoma (RCC).
Female Reproducibility of Results Sensitivity and Specificity Humans Retrospective Studies Tomography, X-Ray Computed Diagnosis, Differential Kidney Neoplasms Carcinoma, Renal Cell Male Middle Aged Support Vector Machine Angiomyolipoma
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Effect of abiotic and biotic stress factors analysis using machine learning methods in zebrafish.

Comparative biochemistry and physiology. Part D, Genomics & proteomics Nov 11, 2017
In order to understand the mechanisms underlying stress responses, meta-analysis of transcriptome is made to identify differentially expressed genes (DEGs) and their biological, molecular and cellular mechanisms in response to stressors. The present ...
Oligonucleotide Array Sequence Analysis Up-Regulation Fish Proteins Gene Ontology Principal Component Analysis Transcription Factors Support Vector Machine Meta-Analysis as Topic Zebrafish MicroRNAs Supervised Machine Learning Down-Regulation Animals Stress, Physiological Gene Expression Profiling
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Relevant Features Selection for Automatic Prediction of Preterm Deliveries from Pregnancy ElectroHysterograhic (EHG) records.

Journal of medical systems Nov 11, 2017
In this study, we proposed an approach able to predict whether a pregnant woman with contractions would give birth earlier than expected (i.e., before the 37 week of gestation (WG)). It only processes non-invasive electrohysterographic (EHG) signals...
Reproducibility of Results Pregnancy Signal Processing, Computer-Assisted Humans Female Support Vector Machine Electromyography Algorithms Premature Birth Uterus
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Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms.

Computer methods and programs in biomedicine Nov 9, 2017
BACKGROUND AND OBJECTIVE: Surface electromyography (sEMG) based muscle fatigue research is widely preferred in sports science and occupational/rehabilitation studies due to its noninvasiveness. However, these signals are complex, multicomponent and h...
Muscle Contraction Muscle Fatigue Humans Machine Learning Algorithms Models, Theoretical Support Vector Machine Muscle, Skeletal Electromyography Arm Automation
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Prediction of menarcheal status of girls using voice features.

Computers in biology and medicine Nov 8, 2017
A method for evaluating the menarcheal status of girls on the basis of their voice features is presented in the paper. The registration procedure consists of voice recording and measuring 20 anthropological features. The input feature vector is a com...
Female Adolescent Support Vector Machine Signal Processing, Computer-Assisted Voice Menarche Humans Algorithms
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Code-modulated visual evoked potentials using fast stimulus presentation and spatiotemporal beamformer decoding.

Scientific reports Nov 8, 2017
When encoding visual targets using various lagged versions of a pseudorandom binary sequence of luminance changes, the EEG signal recorded over the viewer's occipital pole exhibits so-called code-modulated visual evoked potentials (cVEPs), the phase ...
Young Adult Adult Male Algorithms Brain Female Humans Brain-Computer Interfaces Adolescent Photic Stimulation Evoked Potentials, Visual Support Vector Machine Visual Perception Electroencephalography
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