2nd International Workshop on Inductive Modeling (IWIM 2007)

Venue: CTU Prague

Location: Prague, Czech Republic

Event Date/Time: Sep 23, 2007 End Date/Time: Sep 26, 2007
Registration Date: Sep 01, 2007
Early Registration Date: Jul 01, 2007
Abstract Submission Date: May 15, 2007
Paper Submission Date: Jul 31, 2007
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IWIM 2007 Workshop Theme:
State, progress and outlook of inductive modeling, real-world applications of GMDH

The 2nd International Workshop on Inductive Modeling (IWIM07) will be held in Prague on September 23-26, 2007. The initial Workshop took place in Kyiv, Ukraine, in July 2005 following the International Conference on Inductive Modeling (ICIM'2002) in Lviv, Ukraine, in May 2002. The series of conferences and workshops is the only international forum that focuses on theory, algorithms, applications, solutions, and new developments of data mining and knowledge extraction technologies which originate from Ivakhnenko’s Group Method of Data Handling (GMDH) as a typical inductive modeling method. Built on principles of self-organization inductive modeling has been developing and using in several key areas for over 30 years now and can be found in data mining technologies like Polynomial Neural Networks, Adaptive Learning Networks, or Statistical Learning Networks. More recent developments also utilize Genetic Algorithms or the idea of Active Neurons and multileveled self-organization to build models from data.

The motivation of this 2nd workshop is to analyze the state-of-the-art of modeling methods that inductively generate models from data, to discuss concepts of an automated knowledge discovery workflow, to share new ideas on model validation and visualization, to present novel applications in different areas, and to give inspiration and background on how inductive modeling can evolve and contribute given the current global challenges.

Topics of interest include, but are not limited to:

* Principles and theoretical fundamentals of inductive modeling.
* Optimal complexity of inductive models, regularization, selection criteria.
* Validation of inductively generated models, including plausibility, significance or sensitivity analysis.
* Optimization and evolution of inductive models and neural networks.
* Inductive models ensembles, neural networks ensembles.
* Knowledge mining, relationships detection by means of inductive modeling.
* Knowledge discovery workflow (KDD) automation, automated data preprocessing.
* Time series analysis and prediction by means of inductive models.
* Visual data mining, model visualization.
* Real-world applications, including solutions in ecology, sociology, medicine, bioinformatics and temporal and/or spatial modeling problems.
* High-performance computing, including parallel and distributed computing.
* Inductive Modeling vs. Computational Intelligence.