Results 231 to 240 of about 1,003,291 (283)

Support vector machine classification of patients with depression based on resting-state electroencephalography. [PDF]

Asian Biomed (Res Rev News)
Yang CY, Chen YZ.
europepmc   +1 more source

AOPxSVM: A Support Vector Machine for Identifying Antioxidant Peptides Using a Block Substitution Matrix and Amino Acid Composition, Transformation, and Distribution Embeddings. [PDF]

Foods
Li R, Wang H, Yu Q, Cai J, Jiang L, Luo X, Zou Q, Lv Z.   +7 more
europepmc   +1 more source

Predicting Postoperative Recurrence Using a Support Vector Machine for Patients With Esophageal Squamous Cell Carcinoma: Machine Learning Modeling Development and Validation Study.

JMIR Cancer
Xu MQ, Jiang ZS, Liao WY, Kang Y, Feng XY, Jiang K, Jiang Q, Cong ZZ, Luo J, Wu L, Shen Y, Wang FY.   +11 more
europepmc   +1 more source

One size does not fit all: a support vector machine exploration of multiclass cognitive state classifications using physiological measures. [PDF]

Front Neuroergon
Vogl J, O'Brien K, St Onge P.
europepmc   +1 more source

Optimized Adaboost Support Vector Machine-Based Encryption for Securing IoT-Cloud Healthcare Data. [PDF]

Sensors (Basel)
Abushark YB, Hassan S, Khan AI.
europepmc   +1 more source

Classification of Dog Breeds Using Convolutional Neural Network Models and Support Vector Machine. [PDF]

Bioengineering (Basel)
Cui Y, Tang B, Wu G, Li L, Zhang X, Du Z, Zhao W.   +6 more
europepmc   +1 more source

Construction of an oligometastatic prediction model for nasopharyngeal carcinoma patients based on pathomics features and dynamic multi-swarm particle swarm optimization support vector machine. [PDF]

Front Oncol
Li Y, Zhang D, Wang Y, Hu Y, Wen Z, Yang C, Zhou P, Cheng WH.   +7 more
europepmc   +1 more source

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Field support vector machines

Proceedings of the 1st International Conference on Internet of Things and Machine Learning, 2017
Conventional classifiers often regard input samples as identically and independently distributed (i.i.d.). This is however not true in many real applications, especially when patterns occur as groups (where each group shares a homogeneous style). Such tasks are also called field classification. By breaking the i.i.d.
Kaizhu Huang, Haochuan Jiang, Xu-Yao Zhang   +2 more
openaire   +1 more source

Distributed Support Vector Machines

IEEE Transactions on Neural Networks, 2006
A truly distributed (as opposed to parallelized) support vector machine (SVM) algorithm is presented. Training data are assumed to come from the same distribution and are locally stored in a number of different locations with processing capabilities (nodes).
A, Navia-Vazquez, D, Gutierrez-Gonzalez, E, Parrado-Hernandez, J J, Navarro-Abellan   +3 more
openaire   +2 more sources

Fuzzy support vector machines

IEEE Transactions on Neural Networks, 2002
A support vector machine (SVM) learns the decision surface from two distinct classes of the input points. In many applications, each input point may not be fully assigned to one of these two classes. In this paper, we apply a fuzzy membership to each input point and reformulate the SVMs such that different input points can make different contributions ...
Lin, Chun-Fu, Wang, Sheng-De
openaire   +2 more sources