In present study, tow spectral based classification algorithms of support vector machine and neutral network are compared on hyperion image for classification of cluttered units in an ophiolite set.
In order to estimate the quality of DNA data, the classification method of support vector machines (SVM), whose aim is to find an optimal hyperplane separating two different classes, can be used.
We tested the classification accuracy of support vector machine (SVM) and position specific score matrix (PSSM) – the computational methods used by geno2pheno and WebPSSM respectively.
For new gene signatures, we utilize the classification method of support vector machine (SVM) and repeat five-fold cross validation (5F-CV) ten times, using the training dataset as an internal reference.
In the tests for the classification of pressure support and volume support parameters, the highest success rates of 94,4% and 92,2% were obtained by the Bayesian Regulation backpropagation learning algorithm and the Cyclical order weight/bias supervised learning algorithm, respectively.
Looking beyond the lead identification process and with it the classification of molecules, support vector regression (SVR) can be utilized to reveal and address the specific affinity of molecules during the optimization of potential drugs.
The proposed architecture utilizes the concept of binary classification of SVM (Support Vector Machine) and the concept of multiclass classifier of Neural Network to identify the SPAM and category of the data received online.
We defined and applied a machine learning approach for literature classification to support updating of TANTIGEN, a knowledgebase of tumor T-cell antigens.
A recent paper by Ingram et al. identifies a complete hierarchy of EPCs in the vessel wall, providing a new framework for classification of cells supporting endopoiesis akin to that previously established for hematopoiesis.
For example, taxonomic congruence, as defined by [ 12], is the degree to which different classifications of the same taxa support the same groupings.
