Results confirm that the spectral-spatial classification using learning-based feature extraction is able to improve the classification accuracy considerably; that is because spatial features can help to significantly prevent salt-and-pepper noise in the result of pixel-wise classification.
The self-learning classification algorithm CLASSIF1 was capable of accurately predicting RA when these profiles were present.
Recent developments in the direction of target-class-specific scoring methods and machine-learning-based classification models reveals a significant improvement in binding mode and activity prediction [1].
This characterization, in turn, forms the foundation for developing a new machine learning-based classification algorithm which employs these network dynamics features for accurate early warning analysis.
One approach to achieving this goal is multivariate pattern analysis, a machine-learning, pattern classification approach well-suited for high-dimensional neuroimaging data.
To estimate these positional probabilities, we propose a machine-learning-based classification system that we train with data gathered from a real-world OO reference system.
When binary matching is performed, the basic resemblance in (18) can further be exploited to obtain resemblance with the other distance metric-based and machine learning-based classification performance.
The idea of fingerprinting drivers from timestamped sensor data, e.g., controller area network (CAN) protocol records, is not new; many recent studies have shown that identifying a driver using machine learning-based classification is a promising field of research.
In video semantic classification, most of the learning-based approaches require a large training set to achieve good generalization capacity, in which large amounts of labor-intensive manual labeling are ineluctable.
The analytical results revealed that the metaheuristic optimization within machine learning-based classification system exhibited a groutability prediction accuracy of 95.42%, seismic prediction accuracy of 93.96%, soil liquefaction prediction accuracy of 95.18%, and soil collapse prediction accuracy of 95.45%.
