Morik, K.: The representation race - preprocessing for handling time phenomena. In de Mantaras, R.L., Plaza, E., eds.: Proceedings of the European Conference on Machine Learning 2000 (ECML 2000). Volume 1810 of Lecture Notes in Artificial Intelligence., Berlin, Heidelberg, New York, Springer Verlag Berlin (2000) Home/Search Document Details and Download
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SVM Kernels for Time Series Analysis - Rüping (2001) (Correct)
....sales that are influenced by changing tastes or changing contents of web sites) A reason for its complexity is that time can be represented in a multitude of different representations. As always, an effect that is obvious in one representation may be very much hidden in another representation. In [8] Morik gives a discussion of different representations and learning tasks for time phenomena. Of all the possible representation of time, time series, i.e. the representation of a time dependent observation x t at (usually equidistant) time points t as tuples (t; x t ) are the most common. ....
Katharina Morik. The representation race -- preprocessing for handling time phenomena. In Ramon Lopez de Mantaras and Enric Plaza, editors, Machine Learning: ECML
Features for Learning Local Patterns in Time-Stamped Data - Morik, Köpcke (2005) Self-citation (Morik) (Correct)
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Morik, K.: The representation race - preprocessing for handling time phenomena. In de Mantaras, R.L., Plaza, E., eds.: Proceedings of the European Conference on Machine Learning 2000 (ECML 2000). Volume 1810 of Lecture Notes in Artificial Intelligence., Berlin, Heidelberg, New York, Springer Verlag Berlin (2000)
Support Vector Machines And Learning About Time - Stefan Uping And (2003) Self-citation (Morik) (Correct)
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Katharina Morik, "The representation race - preprocessing for handling time phenomena," in Proc. of the European Conference on Machine Learning (ECML 2000.
Features for Learning Local Patterns in Time-Stamped Data - Morik, Köpcke (2005) Self-citation (Morik) (Correct)
No context found.
Morik, K.: The representation race - preprocessing for handling time phenomena. In de Mantaras, R.L., Plaza, E., eds.: Proceedings of the European Conference on Machine Learning 2000 (ECML 2000). Volume 1810 of Lecture Notes in Artificial Intelligence., Berlin, Heidelberg, New York, Springer Verlag Berlin (2000)
End-User Access to Multiple Sources - Incorporating.. - Morik, Hüppe, Unterstein (2002) Self-citation (Morik) (Correct)
....in [1] The results of a KDD case change as soon as the content of the database is changed. Therefore, a stored question together with a KDD result will soon be outdated. Hence, the KDD case should be stored as a sequence of steps which is executable on the database. The MiningMart project [25] [26] develops a system which stores executable KDD cases. The kernel of the project is an ontology of cases,i.e. the model of meta data which describe a case. The model of meta data, M4, is structured into a part describing the database and a part describing the sequence of steps. The meta data of ....
Morik, K.: The representation race - preprocessing for handling time phenomena. In de Mantaras, R.L., Plaza, E., eds.: Proceedings of the European Conference on Machine Learning 2000 (ECML 2000). Volume 1810 of Lecture Notes in Arti cial Intelligence., Berlin, Heidelberg, New York, Springer Verlag Berlin (2000)
Support Vector Machines and Learning about Time - Rüping, Morik (2003) Self-citation (Morik) (Correct)
....control (control the process behind the time series to generate certain future values) For Machine Learning, an overall theory of temporal analysis is much less developed. Learning tasks are usually taken from speci c real world problems and representations are often constructed ad hoc. Morik [15] di erentiates between two di erent aspects of time, the linear precedence of events and immediate dominance of temporal categories. These terms originate from natural language theory [9] Immediate dominance refers to the construction of higher level categories of the time dependent elements, ....
Katharina Morik. The representation race - preprocessing for handling time phenomena. In Ramon Lopez de Mantaras and Enric Plaza, editors, Proceedings of the European Conference on Machine Learning 2000.
Mining Mart: Metadata-Driven Preprocessing - Zücker, Kietz, Vaduva (2001) (Correct)
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K. Morik. The representation race - preprocessing for handling time phenomena. In Proc. of the European Conference on Machine Learning, ECML-2000. Springer Verlag, 2000.
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