Results 41 to 50 of about 8,989,075 (297)
Binarized support vector machines [PDF]
, 2007 The widely used Support Vector Machine (SVM) method has shown to yield very good results in Supervised Classification problems. Other methods such as Classification Trees have become more popular among practitioners than SVM thanks to their ...
Martin-Barragan, Belen +2 more
core +1 more source
Novel folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing [PDF]
, 2014 As a widely used approach for feature extraction and data reduction, Principal Components Analysis (PCA) suffers from high computational cost, large memory requirement and low efficacy in dealing with large dimensional datasets such as Hyperspectral ...
Han, Junwei +6 more
core +4 more sources
Support vector machine classifiers by non-Euclidean margins
Mathematical Foundations of Computing, 2020 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ying Lin 0002, Qi Ye 0007
openaire +2 more sources
Deep Learning Pose Estimation for Phenotyping of Co‐Occurring Hyperkinetic Movement Disorders
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To explore whether routine outpatient video combined with deep learning‐based pose estimation and clinically interpretable kinematic features can support multi‐label phenotyping of co‐occurring hyperkinetic movement disorders (HMDs).
Laura Cif +17 more
wiley +1 more source
Robust ASR using Support Vector Machines [PDF]
, 2007 The improved theoretical properties of Support Vector Machines with respect to other machine learning alternatives due to their max-margin training paradigm have led us to suggest them as a good technique for robust speech recognition. However, important
F. Díaz-de-María +17 more
core +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Progression independent of relapse activity is a major determinant of long‐term disability in multiple sclerosis, but its immunopathologic basis remains incompletely understood. We investigated whether relapse‐independent progression in radiologically stable relapsing–remitting multiple sclerosis is associated with distinct ...
Antonio Bruno +19 more
wiley +1 more source
Fault Diagnosis of Reciprocating Compressors Using Revelance Vector Machines with A Genetic Algorithm Based on Vibration Data [PDF]
This paper focuses on the development of an advanced fault classifier for monitoring reciprocating compressors (RC) based on vibration signals. Many feature parameters can be used for fault diagnosis, here the classifier is developed based on a relevance
Gu, Fengshou +3 more
core +3 more sources
Grey Wolf Optimizes Mixed Parameter Multi-Classification Twin Support Vector Machine
Jisuanji kexue yu tansuo, 2020 Twin support vector machine (TWSVM) is an efficient binary classification algorithm based on support vector machine (SVM). Since most of the problems in reality are multi-classified, it is very important to extend binary classification twin support ...
ZHOU Guangyue, LI Kewen, LIU Wenying, SU Zhaoxin
doaj +1 more source
Laplacian Twin Support Vector Machine With Pinball Loss for Semi-Supervised Classification
IEEE Access, 2023 Semi-supervised learning utilizes labeled data and the geometric information in the unlabeled data to construct a model whereas supervised learning makes use of the only label data.
Vipavee Damminsed +2 more
doaj +1 more source
A new boosting design of Support Vector Machine classifiers [PDF]
Information Fusion, 2015 Boosting algorithms pay attention to the particular structure of the training data when learning, by means of iteratively emphasizing the importance of the training samples according to their difficulty for being correctly classified. If common kernel Support Vector Machines (SVMs) are used as basic learners to construct a Real AdaBoost ensemble, the ...
Efraín Mayhua-López +2 more
openaire +3 more sources