Results 11 to 20 of about 98,638 (216)

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics. [PDF]

Cancer Genomics Proteomics, 2018
Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the ...
Huang S, Cai N, Pacheco PP, Narrandes S, Wang Y, Xu W.   +5 more
europepmc   +4 more sources

AML-SVM: Adaptive Multilevel Learning with Support Vector Machines [PDF]

2020 IEEE International Conference on Big Data (Big Data), 2020
10 pages, 5 tables, 3 figures, IEEE BigData ...
Ehsan Sadrfaridpour, Korey Palmer, Ilya Safro   +2 more
openaire   +2 more sources

SUPPORT VECTOR MACHINES (SVMs) WITH UNIVERSAL KERNELS

Applied Artificial Intelligence, 2011
In this paper, a universal kernel function is introduced that could improve the classification accuracy of Support Vector Machines (SVMs) for both linear and nonlinear data sets. A class of universal kernel functions on the basis of the properties of the common kernels is proposed, which can find numerous applications in practice.
E. A. Zanaty, Ashraf Afifi
openaire   +2 more sources

A geometric approach to Support Vector Machine (SVM) classification

IEEE Transactions on Neural Networks, 2006
The geometric framework for the support vector machine (SVM) classification problem provides an intuitive ground for the understanding and the application of geometric optimization algorithms, leading to practical solutions of real world classification problems. In this work, the notion of "reduced convex hull" is employed and supported by a set of new
Mavroforakis, M.E., Theodoridis, S.
openaire   +4 more sources

Support Vector Machines (SVM) as a Technique for Solvency Analysis [PDF]

, 2009
This paper introduces a statistical technique, Support Vector Machines (SVM), which is considered by the Deutsche Bundesbank as an alternative for company rating. A special attention is paid to the features of the SVM which provide a higher accuracy of company classification into solvent and insolvent. The advantages and disadvantages of the method are
Laura Auria, Rouslan A. Moro
openaire   +2 more sources

Unique circulating miRNAome signatures captured with distinct molecular states in Lynch syndrome and sporadic colorectal cancer

Molecular Oncology, EarlyView.
Circulating microRNA profiles distinguish nonmalignant Lynch syndrome from cancer‐associated Lynch syndrome, sporadic colorectal cancer, and healthy controls. Age‐stratified analysis reveals disease‐specific regulatory states, and a six‐miRNA panel shows concordant separation in an independent colorectal tissue cohort.
Ramadhani Salum Chambuso, Md Shohel Ul Islam, Rosa M Xicola, Raj Ramesar, Xavier Llor, Sinclair Steele   +5 more
wiley   +1 more source

The impact of leukocyte‐associated RNA retention on platelet RNA‐based cancer detection

Molecular Oncology, EarlyView.
Platelets carry RNA signals that may help detect cancer, but platelet samples can also retain RNA from white blood cells. We found that this retained white blood cell signal did not significantly affect machine learning cancer classification, suggesting that the cancer‐related RNA signal in platelets is largely independent of leukocyte‐associated RNA ...
Michał Sieczczyński, Krzysztof Pastuszak, Anna Samelak‐Czajka, Zuzanna Cemka, Paulina Jackowiak, Tomasz Stefaniak, Sylwia Łapińska‐Szumczyk, Jacek Jassem, Anna J. Żaczek, Matthew T. Rondina, Myron G. Best, Sjors G. J. G. In 't Veld, Anna Supernat   +12 more
wiley   +1 more source

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, Diane Demailly, Gabriella A. Horvàth, Juan Dario Ortigoza Escobar, Nathalie Dorison, Mayté Castro Jiménez, Cécile A. Hubsch, Thomas Wirth, Gun‐Marie Hariz, Sophie Huby, Morgan Dornadic, Zohra Souei, Muhammad Mushhood Ur Rehman, Simone Hemm, Mehdi Boulaymen, Eduardo M. Moraud, Jocelyne Bloch, Xavier Vasques   +17 more
wiley   +1 more source

CSF Cytokine Network Organization Predicts Progression Independent of Relapse and MRI Activity in Multiple Sclerosis

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, Matteo Conti, Ettore Dolcetti, Federica Azzolini, Angela Borrelli, Roberta Fantozzi, Mario Stampanoni Bassi, Giovanni Galifi, Giuseppe Maccarrone, Luca Montaguti, Marco Cervigni, Maddalena Dal Pozzo, Roberto Furlan, Annamaria Finardi, Girolama Alessandra Marfia, Marco Salvetti, Antonella Conte, Fabio Buttari, Diego Centonze, Luana Gilio   +19 more
wiley   +1 more source

Fast Support Vector Machine Classification using linear SVMs [PDF]

18th International Conference on Pattern Recognition (ICPR'06), 2006
We propose a classification method based on a decision tree whose nodes consist of linear Support Vector Machines (SVMs). Each node defines a decision hyperplane that classifies part of the feature space. For large classification problems (with many Support Vectors (SVs)) it has the advantage that the classification time does not depend on the number ...
Karina Zapien Arreola, Janis Fehr, Hans Burkhardt   +2 more
openaire   +1 more source