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Data preprocessing is important in machine learning, data mining, and pattern recognition. In particular, selecting relevant features in high- dimensional data is often necessary to efficiently construct models that accurately describe the data. For example, many lazy learning algorithms (like k- Nearest Neighbor) rely on feature-based distance metrics to compare input patterns for the purpose of...
Database approaches are widely used in structural bioinformatics, since ab initio techniques are often computationally prohibitive, and the structure of biological macromolecules are typically derived from a limited set of motifs. There are several issues and challenges that arise when developing methods to enable efficient database retrieval. For example, how can complex data be represented efficiently,...
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