Beyond Tripeptides Two-Step Active Machine Learning for Very Large Data sets

Distributed Learning on Very Large Data Sets

One approach to learning from intractably large data sets is to utilize all the training data by learning models on tractably sized subsets of the data. The subsets of data may be disjoint or partially overlapping. The individual learned models may be combined into a single model or a voting approach may be used to combine the classi cations of a set of models. An approach to learning models in...

Soft Clustering for Very Large Data Sets

Clustering is regarded as one of the significant task in data mining and has been widely used in very large data sets. Soft clustering is unlike the traditional hard clustering which allows one data belong to two or more clusters. Soft clustering such as fuzzy c-means and rough k-means have been proposed and successfully applied to deal with uncertainty and vagueness. However, the influx of ver...

Decision Tree Learning on Very Large Data Sets

Consider a labeled data set of 1 terabyte in size. A salient subset might depend upon the users interests. Clearly, browsing such a large data set to find interesting areas would be very time consuming. An intelligent agent which, for a given class of user, could provide hints on areas of the data that might interest the user would be very useful. Given large data sets having categories of sali...

Multiresolution Data Handling for Visualization of Very Large Data Sets

Ensembles of data-reduction-based classifiers for distributed learning from very large data sets

An ensemble of classifiers is a set of classification models and a method to combine their predictions into a joint decision. They were primarily devised to improve classification accuracies over individual classifiers. However, the growing need for learning from very large data sets has opened new application areas for this strategy. According to this approach, new ensembles of classifiers hav...