Accurate summary data is of paramount concern in data warehouse systems; however, there have been few attempts to completely characterize the ability to summarize measures. The su...
Combining multiple global models (e.g. back-propagation based neural networks) is an effective technique for improving classification accuracy by reducing a variance through manipu...
Abstract Traditional financial analysis systems utilize lowlevel price data as their analytical basis. For example, a decision-making system for stock predictions regards raw price...
We consider the problem of multiclass classification where both labeled and unlabeled data points are given. We introduce and demonstrate a new approach for estimating a distribut...
Unsupervised learning methods often involve summarizing the data using a small number of parameters. In certain domains, only a small subset of the available data is relevant for ...