Optimizing connectionist datasets with ConSTrainer

In many connectionist applications training datasets lie usually selected randomly from databases. Such datasets are not guaranteed to represent the entire task domain fairly, and there is often a requirement to pre-process the dataset for compression and normalization. ConSTrainer is a window-based toolkit dedicated to the task of collecting and optimizing datasets for training connectionist networks. One of the most important features of ConSTrainer is its support for optimizing and validating training datasets. Two types of optimization features are examined in this paper: malicious training vector detection, and dataset compression. This paper describes ConSTrainer´s facilities for optimizing connectionist datasets and demonstrates their utilization in a non-trivial application for diagnostic decision support in Histopathology