Results 41 to 50 of about 613,158 (317)

A Comparative Study between SVM and Fuzzy Inference System for the Automatic Prediction of Sleep Stages and the Assessment of Sleep Quality

open access: yesEAI Endorsed Transactions on Pervasive Health and Technology, 2015
This paper compares two supervised learning algorithms for predicting the sleep stages based on the human brain activity. The first step of the presented work regards feature extraction from real human electroencephalography (EEG) data together with its ...
John Gialelis   +4 more
doaj   +1 more source

EEG-induced Fear-type Emotion Classification Through Wavelet Packet Decomposition, Wavelet Entropy, and SVM

open access: yesHittite Journal of Science and Engineering, 2022
Among the most significant characteristics of human beings is their ability to feel emotions. In recent years, human-machine interface (HM) research has centered on ways to empower the classification of emotions.
Ahmet Ergun Gümüş   +2 more
doaj   +1 more source

Performance and optimization of support vector machines in high-energy physics classification problems

open access: yes, 2016
In this paper we promote the use of Support Vector Machines (SVM) as a machine learning tool for searches in high-energy physics. As an example for a new- physics search we discuss the popular case of Supersymmetry at the Large Hadron Collider.
Krücker, Dirk   +2 more
core   +1 more source

Homogenous Ensemble Phonotactic Language Recognition Based on SVM Supervector Reconstruction [PDF]

open access: yes, 2014
Currently, acoustic spoken language recognition (SLR) and phonotactic SLR systems are widely used language recognition systems. To achieve better performance, researchers combine multiple subsystems with the results often much better than a single SLR ...
Johnson, Michael T   +3 more
core   +2 more sources

Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness

open access: yesSustainability, 2020
The aim of this study was twofold: (1) to assess the performance accuracy of support vector machine (SVM) models with different kernels to predict rock brittleness and (2) compare the inputs’ importance in different SVM models.
D. Jahed Armaghani   +5 more
semanticscholar   +1 more source

SVM-Based Channel Estimation and Data Detection for One-Bit Massive MIMO systems [PDF]

open access: yes, 2020
The use of low-resolution Analog-to-Digital Converters (ADCs) is a practical solution for reducing cost and power consumption for massive Multiple-Input-Multiple-Output (MIMO) systems.
Nguyen, DHN   +2 more
core   +2 more sources

Criticality and the Onset of Ordering in the Standard Vicsek Model [PDF]

open access: yes, 2012
Experimental observations of animal collective behavior have shown stunning evidence for the emergence of large-scale cooperative phenomena resembling phase transitions in physical systems.
Albano, Ezequiel V.   +2 more
core   +2 more sources

Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN)

open access: yes, 2018
Landslide is a natural hazard that results in many economic damages and human losses every year. Numerous researchers have studied landslide susceptibility mapping (LSM), each attempting to improve the accuracy of the final outputs.
B. Kalantar   +4 more
semanticscholar   +1 more source

A Divide-and-Conquer Solver for Kernel Support Vector Machines [PDF]

open access: yes, 2013
The kernel support vector machine (SVM) is one of the most widely used classification methods; however, the amount of computation required becomes the bottleneck when facing millions of samples.
Dhillon, Inderjit S.   +2 more
core   +1 more source

Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization

open access: yes, 2017
We aimed to evaluate computer-aided diagnosis (CADx) system for lung nodule classification focusing on (i) usefulness of gradient tree boosting (XGBoost) and (ii) effectiveness of parameter optimization using Bayesian optimization (Tree Parzen Estimator,
Kojima, Ryosuke   +6 more
core   +2 more sources

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