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Today, many private households as well as broadcasting or film companies own large collections of digital music plays. These are time series that differ from, e.g., weather reports or stocks market data. The task is normally that of classification, not prediction of the next value or recognizing a shape or motif. New methods for extracting features that(More)
The paper describes a case study in combining diierent methods for acquiring medical knowledge. Given a huge amount of noisy, high dimensional numerical time series data describing patients in intensive care, the support vector machine is used to learn when and how to change the dose of which drug. Given medical knowledge about and expertise in clinical(More)
We present a freely available benchmark dataset for audio classification and clustering. This dataset consists of 10 seconds samples of 1886 songs obtained from the Garage-band site. Beside the audio clips themselves, textual meta data is provided for the individual songs. The songs are classified into 9 genres. In addition to the genre information , our(More)
Machine learning techniques are often used for supporting a knowledge engineer in constructing a model of part of the world. Diierent learning algorithms contribute to diierent tasks within the modeling process. Integrating several learning algorithms into one system allows it to support several modeling tasks within the same framework. In this paper, we(More)
The representation formalism as well as the representation language is of great importance for the success of machine learning. The representation formalism should be expressive, efficient, useful, and applicable. First-order logic needs to be restricted in order to be efficient for inductive and deductive reasoning. In the field of knowledge representation(More)
Two topics are treated here First, we present a user model pattcrncd after the stereotype approach (Rich, 1979) This model surpasses Rich's model with respect to its greater flexibility in the construction of user profiles, and its trcat,ment of positive and negative arguments. Second, we present an inference machine This machine treats uncertain knowledge(More)
Machine learningcan be a most valuable tool for improvingthe exibilityand eeciency of robot applications. Many approachesto applying machine learning to robotics are known. Some approaches enhance the robot's high-level processing, the planning capabilities. Other approaches enhance the low-level processing, the control of basic actions. In contrast, the(More)
Operational protocols are a valuable means for quality control. However, developing operational protocols is a highly complex and costly task. We present an integrated approach involving both intelligent data analysis and knowledge acquisition from experts that support the development of operational protocols. The aim is to ensure high quality standards for(More)