Exact(60)
Eye movement directions in response to a visual stimulus for tracking an object are classified using ensemble classifiers based on bagging and adaptive boosting algorithms.
Subsequently, the remaining low-confidence points are classified using the trained classifier.
The extracted feature vectors are classified using a SVM classifier.
In [26], temporal features (pre-RR-interval, post-RR-intervals, average RR-intervals, and local average RR-interval), and morphological features (ST-based, WT-based, and combinations) are extracted, and five classes (N, S, V, F, Q) are classified using a multilayer perceptron neural network classifier with an average accuracy of 97.5 %.
TES systems are classified using different types of criteria.
For this, types of rural areas are classified using demographic and socio-economic indicators.
Land covers are classified using Landsat images from year 2001 and 2011.
These features are classified using complex-valued neural network (CVANN) algorithm.
In the mix microstructure, aggregates and sand are classified using simple sieve analysis test.
The feature vectors are classified using optimized discriminative subspace clustering (ODiSC).
Congenital anomalies are classified using the International Classification of Diseases codes and 10 monitoring groups.
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