This paper tackles the topic of data anonymization from a vector quantization point of view.
State-of-the-art machine learning algorithms require large amount of high quality data. In practice, however, the sample size is commonly low and data is imbalanced along different class labels.
Cloud computing infrastructures are being challenged by an increasing demand for evolved cloud services characterised by tight interactivity levels and different performance requirements of data-in
Data cleaning may involve the acquisition, at some effort or expense, of high-quality data.
Experience is the name everyone gives to their mistakes. Automatically Switched Optical Networks (ASON) get a lot of tradion in the market place.
Problems in our air transport system are prominent in the news: delayed and canceled flights, overworked controllers, and near mid-air collisions make regular headlines.
Multiple classifier methods are effective solutions to difficult pattern recognition problems. However, empirical successes and failures have not been completely explained.
For a classification problem that is implicitly represented by a training set, analysis of data complexity provides a linkage between context and solution.
We study the behavior of XCS, a classifier based on genetic algorithms.
A compression server in a PACS environment has to deal with images of different types and sizes.
