Sentence examples for models can learn from inspiring English sources

Classical CNN models can learn two-dimensional filters on spectrograms directly.

Since LML models can learn wrong knowledge as well, therefore, the model needs to have a strong filtering mechanism.

Indeed, hierarchical feature learning using the CNN deep models can learn specific feature representation automatically, which is difficult for most traditional machine learning techniques.

Here, we show how, with the appropriate architectures, deep learning models can learn features directly from a naive feature (i.e., the log power spectrogram in this work).

However, tests show that neither model encompasses the other, so that both models can learn from each other when it comes to improving the accuracy of forecasts.

The ANN models can learn and adapt, from a data set, and they have the ability to capture non-linear relationships between variables which are also advantages of these models.

It is shown that the model can learn categories by developing prototype representations strictly from exposition to specific exemplars.

The model can learn what an occupant normally does during the day from unsegmented training data and then perform online activity classification, segmentation and abnormality detection.

Two experiments are conducted on unicursal shape learning to investigate whether the proposed model can learn the function without any shape information for visual processing.

Based on the first experiment, the model can learn 15 drawing sequences for three types of pictures, acquiring associative memory for drawing motions through the bottom-up learning process.

We validate our model through intensive simulations showing that our model can learn a user behavior and is able to predict several activities helping thus in optimizing these systems for a better performance.