Towards a general theory of action and time
Artificial Intelligence
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Interactive theory revision: an inductive logic programming approach
Interactive theory revision: an inductive logic programming approach
The nature of statistical learning theory
The nature of statistical learning theory
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Fast discovery of association rules
Advances in knowledge discovery and data mining
Making large-scale support vector machine learning practical
Advances in kernel methods
Data preparation for data mining
Data preparation for data mining
Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Concept Formation and Knowledge Revision
Concept Formation and Knowledge Revision
Inductive Logic Programming: From Machine Learning to Software Engineering
Inductive Logic Programming: From Machine Learning to Software Engineering
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
Making Robots Smarter: Combining Sensing and Action through Robot Learning
Making Robots Smarter: Combining Sensing and Action through Robot Learning
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
An Region-Based Learning Approach to Discovering Temporal Structures in Data
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Biochemical Knowledge Discovery Using Inductive Logic Programming
DS '98 Proceedings of the First International Conference on Discovery Science
Stochastic Propositionalization of Non-determinate Background Knowledge
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
A Multistrategy Approach to the Classification of Phases in Business Cycles
ECML '02 Proceedings of the 13th European Conference on Machine Learning
End-User Access to Multiple Sources - Incorporating Knowledge Discovery into Knowledge Management
PAKM '02 Proceedings of the 4th International Conference on Practical Aspects of Knowledge Management
Distance and Feature-Based Clustering of Time Series: An Application on Neurophysiology
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
Hybrid assistance in KDD task definition
SMO'05 Proceedings of the 5th WSEAS international conference on Simulation, modelling and optimization
Mining time series with mine time
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
Features for learning local patterns in time-stamped data
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
From local to global analysis of music time series
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
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Designing the representation languages for the input, LE, and output, LH, of a learning algorithm is the hardest task within machine learning applications. This paper emphasizes the importance of constructing an appropriate representation LE for knowledge discovery applications using the example of time related phenomena. Given the same raw data - most frequently a database with time-stamped data - rather different representations have to be produced for the learning methods that handle time. In this paper, a set of learning tasks dealing with time is given together with the input required by learning methods which solve the tasks. Transformations from raw data to the desired representation are illustrated by three case studies.