Results 81 to 90 of about 7,794,647 (291)
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
Support Vector Machine and Generalization
Journal of Advanced Computational Intelligence and Intelligent Informatics, 2004 The support vector machine (SVM) has been extended to build up nonlinear classifiers using the kernel trick. As a learning model, it has the best recognition performance among the many methods currently known because it is devised to obtain high performance for unlearned data.
openaire +2 more sources
Support Vector Machines in R [PDF]
Being among the most popular and efficient classification and regression methods currently available, implementations of support vector machines exist in almost every popular programming language.
Kurt Hornik +2 more
core
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
Convolutional Support Vector Machine
CoRR, 2020 The support vector machine (SVM) and deep learning (e.g., convolutional neural networks (CNNs)) are the two most famous algorithms in small and big data, respectively. Nonetheless, smaller datasets may be very important, costly, and not easy to obtain in a short time.
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Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Autoimmune glial fibrillary acidic protein astrocytopathy (GFAP‐A) is an inflammatory central nervous system disorder with variable outcomes. Relapse occurs in a subset of patients, but early predictors remain unclear. We aimed to identify admission‐available features associated with 1‐year recurrence and develop an interpretable ...
Qingting Hong +10 more
wiley +1 more source
Catenary Support Vector Machines [PDF]
, 2008 Many problems require making sequential decisions. For these problems, the benefit of acquiring further information must be weighed against the costs. In this paper, we describe the catenary support vector machine(catSVM), a margin-based method to solve sequential stopping problems.
Kin Fai Kan, Christian R. Shelton
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Aplikasi Metode Cross Entropy untuk Support Vector Machines
Jurnal Teknik Industri, 2012 Support vector machines (SVM) is a robust method for classification problem. In the original formulation, the dual form of SVM must be solved by a quadratic programming in order to get the optimal solution.
Budi Santosa, Tiananda Widyarini
doaj +1 more source
A computational analysis of optimization models for Support Vector Machines [PDF]
, 2012 Il presente lavoro di tesi consiste in un'analisi computazionale, condotta previa la necessaria implementazione software, del comportamento matematico del modello di ottimizzazione utilizzato dall'approccio delle Support Vector Machines. Tale valutazione
Petterle, Lucia
core
What Do Large Language Models Know About Materials?
Advanced Engineering Materials, EarlyView.If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer +2 more
wiley +1 more source