Ensemble methods are learning algorithms that construct a set of classi ers and then classify new data points by taking a (weighted) vote of their predictions. The original ensembl...
Successful secondary structure predictions provide a starting point for direct tertiary structure modelling, and also can significantly improve sequence analysis and sequence-stru...
For a presented case, a Bayesian network classifier in essence computes a posterior probability distribution over its class variable. Based upon this distribution, the classifier...
Linda C. van der Gaag, Silja Renooij, Wilma Steene...
Topology preserving mappings are great tools for data visualization and inspection in large datasets. This research presents a combination of several topology preserving mapping mo...
In this paper, we introduce a domain-independent classification framework based on both k-nearest neighbor and Naïve Bayesian classification algorithms. The architecture of our s...