The k-means procedure identifies relatively homogenous subgroups while maximizing the variability between clusters and is able to handle larger amounts of classification variables.
Traditional remote sensing images classification methods focused on using a large amount of labeled target data to train an efficient classification model.
In practice, for efficient classification of large amount of miRNAs, the original input sequences and the additional input sequences can be genome-wide collections of miRNAs.
By using this language, our ontology can benefit from a large amount of support tools for editing, automatic classification, etc. OWL DL is also one of the languages accepted by the OBO consortium.
As a consequence, operators are investing a large amount of money to investigate traffic modeling and classification through packet inspection in order to better understand the characteristic of today's cellular data traffic.
To make sense of the large amount of transcriptome information obtained, functional and pathway classification of significantly altered genes was completed.
Classification accuracy of different methods are shown in Figure 3. Due to large amount of noise and limited information at earlier time points, classification using data from these points is close to random, causing difficulties for IBIS.
It is systems biology modeling that relieves the bottleneck of processing this large amount of RNAi screen image data by providing an efficient means of classification.
Objective: Clinical classifications attempt to summarize a large amount of information in a few indices.
This, combined with a large amount of redundant information, could have a potentially negative effect on classification accuracy.
This shows the fact that using a large amount of social media data from the Internet, without style-based classification, gives no benefit.
