SVM is a state-of-the-art supervised learning algorithm, well suited for large-scale text categorization tasks, and robust on large feature spaces.
Although many studies have aimed at large-scale and thorough categorization of human HK genes, a meaningful consensus has yet to be reached.
In spite of the breadth and diversity of the existing ontologies, none provides a comprehensive means of classifying bioinformatics operations, types of data and identifiers, data formats and topics in a way that is suitable for large-scale semantic annotations and categorization of bioinformatics resources.
Along this line, Strauss and Allen (2008) carried out the first large-scale experiment with laymen participants for emotion type categorization and intensity rating.
In future research, the reward categorization will therefore be part of a large-scale quantitative validation study.
We approach the problem of large-scale satellite image browsing from a content-based retrieval and semantic categorization perspective.
In these cases, even a moderate reduction in plating efficiency may result in lack of mutant recovery, resulting in categorization of the gene as "essential" in a large-scale, genome-wide mutagenesis.
Only through such categorization we can proceed to integrate the two large-scale datasets on RNA localization (our data and Lecuyer, 2007).
Later, these powerful deep architectures have been employed in a more challenging problem, fine-grained visual categorization, by either training on datasets from scratch [3], by fine-tuning deep architectures trained on large-scale datasets [4], or by exploiting the previously trained architectures with specific modifications [5].
This categorization suggests that more than half of the predictions might be artifacts, supporting the suspicion that large-scale detection methods (which account for 98% of InvFEST inversions) have a high false-positive discovery rate.
