Sentence examples for techniques classification from inspiring English sources

In the last step, using SVM (Support Vector Machine) and ANN (Artificial Neural Network) algorithms, two commonly used techniques, classification rules developed to predict the source rock class-membership (zones) from well log data.

In addition to stepwise regression and weighted non-linear programming techniques, intelligent learning techniques (artificial neural networks), and inferential techniques (classification and regression trees), were applied to a dataset of 43 countries from the World Competitiveness Yearbook (WCY).

In this article, we propose two new feature extraction techniques: classification terms and hierarchy aspects.

Figure 9 Decision-making techniques classification based on a priori knowledge in the context of noisy sensing.

Recently, Multifactor-Dimensionality Reduction (MDR) approach [7] and tree-based techniques, classification and regression trees (CART) and random forest (RF), have been used to detect interactions in large-scale association studies [8].

The 'Experiment techniques' classification uses a controlled vocabulary of methods used in experiments.

In literature, several techniques are implemented for human detection which includes segmentation techniques, feature extraction techniques, classification-based detections, etc. Segmentation techniques for human detection include template matching [12], foreground detection [2], and background subtraction [13], but these methods failed with multiple humans in a scene.

Our objective was to demonstrate how a statistical technique, classification and regression trees (CART), can identify high-risk clinical clusters.

Depending on the used imaging method and the performed data mining technique, classification accuracies between 90.3% and 97.3% were achieved, only taking values concerning the differentiation between different tumor types into account.

Studies in[13 15] show feature extraction techniques and classification algorithm for stroke identification and classification in uncompressed images.

The chapter explores techniques measuring classification capabilities of feature vectors.