Dimension reduction is popular for learning predictive models in high-dimensional spaces. It can highlight the relevant part of the feature space and avoid the curse of dimensiona...
Each clustering algorithm induces a similarity between given data points, according to the underlying clustering criteria. Given the large number of available clustering technique...
Principal component analyses (PCA) has been widely used in reduction of the dimensionality of datasets, classification, feature extraction, etc. It has been combined with many oth...
Traversals of heterogeneous object structures are the most common operations in schema-first applications where the three key issues are (1) separation of traversal specifications ...
A paramount question faced by technology innovators is whether to license an innovation to other firms, and if so, what type of license it should use. Information technology innov...