Sentence examples for attribute prediction from inspiring English sources

Table 3 Vessel attribute prediction performance, measured as correlation of manual truth and predicted labels for 158,850 images in IMO testing set   Draught Gross tonnage Length Summer deadweight SVM 0.7556 0.8301 0.8696 0.7930 CNN 0.7911 0.7911 0.7911 0.7911.

Table 4 Vessel attribute prediction performance, measured as coefficient of determination between manual truth and predicted labels for 158,850 images in IMO testing set   Draught Gross tonnage Length Summer deadweight SVM 0.598 0.554 0.743 0.481 CNN 0.770 0.419 0.863 0.466.

This research focuses on facial attribute prediction using a novel deep learning formulation, termed as R-Codean autoencoder.

The proposed R-Codean autoencoder is utilized in facial attribute prediction framework which incorporates patch-based weighting mechanism for assigning higher weights to relevant patches for each attribute.

Experimental results on a series of real benchmark data sets suggest that mining the relation between attributes do enhance the performances of attribute prediction and zero-shot classification, compared with state-of-the-art methods.

It employs a regional object detector, recurrent neural network (RNN -based attRNN -baseddiction, attributecoder–decoder language generator embedded with two RNNs to predictionfined and detanlencoder decoder of a given image.

Evolutionary conservations can be obtained by the position specific scoring matrix (PSSM), which has been proved to be highly effective in protein attribute predictions [ 20, 45].

Since the concept of PseAAC was proposed in 2001 [ 29], it has been penetrating into almost all the fields of protein attribute predictions (see, e.g., [ 31– 731).

Generally, multiple features can not only preserve enough discriminative information for protein attribute predictions, but also complement each other to enhance the performance and robustness of a predictor [ 19].

Reflectance values were extracted from bands 1 to 5 and 7. Linear regression (LR) models were calibrated for soil attributes prediction.

Majority of subjects of those paper were in making use of historical data for decision making activities such as cost estimation and product or project attributes prediction and estimation.