This paper describes an approach for selecting instances in regression problems in the cases where observations x are readily available, but obtaining labels y is hard. Given a database of observations, an algorithm inspired by statistical design of experiments and kernel methods is presented...

This paper investigates the use of Design of Experiments in observational studies in order to select informative observations and features for classification. D-optimal plans are searched for in existing data and based on these plans the variables most relevant for classification are determined....

Support Vector Machines (SVMs) have become a popular learning algorithm, in particular for large, high-dimensional classification problems. SVMs have been shown to give most accurate classification results in a variety of applications. Several methods have been proposed to obtain not only a...

The analysis of temporal data is an important issue of current research, because most real-world data either explicitly or implicitly contains some information about time. The key to successfully solving temporal learning tasks is to analyze the assumptions that can be made and prior knowledge...

A workbench for knowledge acquisition and data analysis is presented and its use for the classification of business cycles is investigated. Inductive Logic Programming (ILP) allows to model relations between intervals, e.g. time or value intervals. Moreover, the user of the workbench is...