Sentence examples for model trained from from inspiring English sources

KPCA i j represents the KPCA model trained from the j th image feature of class i.

Ideally, the KPCA model trained from the class which x also belongs to will always give the minimum reconstruction error.

This article reports on a new model – trained from title and abstract words and cited references – that classifies individual articles by research level.

Once this common subspace is derived, one can project unseen test data onto the subspace for classification by a model trained from the source view data, as in this case no labeled target instances are available.

We applied all these measures to an independent test set, where the original MCH is obtained from airborne Lidar, while the predicted MCH is derived using the satellite inputs and the model trained from the training set.

Through the abovementioned method of data collection, the negative effect of S hidden(−1, k) on ESN is effectively eliminated, and the mapping relationship between the WB audio feature F X (m) and the HF spectral envelope F Y (m) can be properly reflected according to the model trained from B(k) and Q k).   3.

Therefore, the original testing cepstra are recognized by the acoustic models trained from the original training cepstra, and the CHN-processed testing cepstra are recognized by the acoustic models trained from the CHN-processed training cepstra).

For classification, the preimages of each image feature f j ∈F will be obtained by all the KPCA models trained from the j th feature.

In this paper, we focus on the distances between a pattern x and its reconstruction results by the kernel PCA models trained from different classes.

The recognition rates of using these KPCAs individually are listed in column 2 to column 4 in Table 3, where LPQK, LBPK, and MSSPK represent the KPCA models trained from LPQ, LBP-TOP, and MSSP, respectively.

The recognition rates of using these KPCAs individually are listed in column 2 to column 4 in Table 2, where CvletK, GLCMK, and LBPK represent KPCA models trained from curvelets, GLCM, and LBP, respectively.