Most existing classification methods extract features from neuroimaging data and then construct a single classifier to perform classification.
Machine learning operates in supervised, unsupervised and semi-supervised settings to perform classification, regression, visualization, clustering, dimensionality reduction, network modeling, graphical modeling, inference and structured prediction.
To perform classification analysis, two different classification techniques i.e., linear discriminant analysis (LDA) and multi-class support vector machines (SVM) were utilized and compared.
A new MATLAB code that provides tools to perform classification of textural images for applications in the geosciences is presented in this paper.
As a case study, we perform classification and retrieval experiments with our algorithm for predicting people׳s emotional states evoked by images, using generic low-level image features.
In addition, we investigate how many frames are necessary for our method to perform classification in the scenario of online action recognition.
We define sentence vector-based models as those which perform classification on the sole basis of a pair of fixed-size sentence representations that are computed independently of one another.
Prediction was performed via a two-step approach: (i) perform classification to determine whether the molecule is active or inactive, (ii) develop a QSAR regression model of active molecules.
To date, most algorithms generally treat clustering learning and classification learning in a sequential or two-step manner, i.e., first execute clustering learning to explore structures in data, and then perform classification learning on top of the obtained structural information.
It is not completely known how humans perform classification tasks.
To perform classification we resort to the Logistic regression algorithm.
