Sentence examples for multiple feature learning from inspiring English sources

In other words, Deep Learning algorithms work as multiple feature learning steps.

Secondly, a DWAE is constructed with multiple WAEs to enhance the unsupervised feature learning ability.

Hierarchical feature learning in Deep Learning extracts multiple layers of non-linear features and then a classifier combines all the features to make predictions [88].

Multi-view dictionary learning is an effective feature learning technique, which learns dictionaries from multiple views of the same object and has achieved state-of-the-art classification results.

Recently, unsupervised feature learning and deep learning methods have shown superior performance in learning efficient representation of images at multiple layers [24, 28 33].

This shows the direct proportionality of multiple features with learning capability for machine learning algorithms.

Our approach, which has focused on integration of multiple features through machine learning techniques, suits well to inclusion of multiple physiologic signals.

Here a machine learning based reliable approach with multiple features oriented statistical learning has been applied, having a clear edge over rule based approaches.

Therefore, this paper describes and analyzes a novel multiple feature recognition framework which incorporates the tight frame learning technique with an adaptive subspace recognition strategy.

These typically leverage the techniques learned from statistical machine learning, coupled with ensemble architectures that create multiple feature detection models.

Unlike the traditional Multiple Kernel Learning (MKL) with the implicit kernels, Multiple Empirical Kernel Learning (MEKL) explicitly maps the original data space into multiple feature spaces via different empirical kernels.