Sentence examples for by using the supervised from inspiring English sources

Meanwhile, it makes the cross-domain gaits for different subjects disperse more separately with a large margin by using the supervised similarity matrix.

The machine learning algorithms are made by using the supervised machine learning methods such as artificial neural network model and local linear neuro-fuzzy models.

Thus, prediction of the 1994 BLC6 vectors from their previous 5BLC, by using the supervised W matrix with 1992_93, gave an average <δ_pred> = 1.25, which decreases to 1.11 by smoothing the matrix indicating that it represents better the 1994 test period.

Table 1 shows the spectral angle distance (SAD) [3] between the most similar endmembers detected by the original ENVI implementation (using the supervised -dimensional visualization tool to derive the final set of endmembers), the PPI approximation described in Section 3 (implemented in the C++ programming language), and our FPGA-based implementation.

The detector obtained 1-pixel-width edges when using the supervised input given by a Canny edge detector.

By using the new NMF method, the supervised knowledge can be effectively preserved, and we seek a matrix factorization which gives a good approximation for both of the data matrix and label matrix.

The supervised classification algorithm is trained by using the 100-year Flood Insurance Rated Maps (FIRM) from the U.S. Federal Emergency Management Agency (FEMA).

12 19 20 All these studies successfully used the supervised regimen by Bohmer et al 62 and have contributed to the recommendation that exercises are used as first line management.

We have compared the results obtained by using four supervised features (namely, classemes, prosemantic features, object bank, and a feature obtained from a Canonical Correlation Analysis) against those obtained by using low-level features.

A successful classification of the prefectures of Greece (in forest fire risk zones) was performed by the expert system by comparing the produced fuzzy expected intervals to each other and by using a supervised machine learning algorithm that assigns a certain weight of forest fire risk to each prefecture (Machine Learning, John Wiley and Sons, 1995).

Because of the large amount of imbalanced data, the building and testing of the supervised model were only possible by using big data supervised classifiers managing imbalance.