Sentence examples for representations for classification from inspiring English sources

In this paper we empirically analyze the importance of sparsifying representations for classification purposes.

Wang et al. [26] study the problem of linked document embedding for classification and propose a linked document embedding framework LDE, which combines link and label information with content information to learn document representations for classification.

With time resolved EEG, we will then report new analyses in three observers revealing how the left and right occipito-temporal regions of the brain extract facial information across spatial frequency bands, over the first 200 ms of processing, to construct decorrelated representations for classification (see [49] for a generic version of this point).

SRC is a discriminative nature of sparse representation for classification.

The experimental results on diverse time series data sets demonstrate that our proposed representation significantly outperforms the original SAX representation and an improved SAX representation for classification.

Therefore, the novel distance-based features proposed in this article are examined over a number of different pattern classification problems and the distance-based features and the original features are concatenated for another new feature representation for classification.

Experimental results on various public face databases demonstrate that the proposed algorithm provides a better feature representation for classification and achieves higher recognition rates compared with several state-of-the-art algorithms.

It uses a generalized representation for classification rules that consists of alternating layers of prediction nodes (represented by ellipses in Fig. 3b) and splitter nodes (represented by rectangles in Fig. 3b).

The feature extraction and recognition algorithm is complex; thus, a new leakage aperture recognition method is proposed that presents a feature extraction algorithm based on the Ensemble Local Mean Decomposition (ELMD -K-L (Kullback-LELMD -K-LodELMD -K-Larse Representation for Classification.

Our approach and GSEA [9] differ with regard to the following aspects: pathway scoring, differential pathway detection, and sample-specific information representation for classification.

Deep learning methods are representation learning methods [20], which allow a machine to be fed with raw data and to automatically learn the representations needed for classification.