EconBiz - Find Economic Literature
    • Logout
    • Change account settings
  • A-Z
  • Beta
  • About EconBiz
  • News
  • Thesaurus (STW)
  • Academic Skills
  • Help
  •  My account 
    • Logout
    • Change account settings
  • Login
EconBiz - Find Economic Literature
Publications Events
Search options
Advanced Search history
My EconBiz
Favorites Loans Reservations Fines
    You are here:
  • Home
  • Search: subject:"Learning Classifier Systems"
Narrow search

Narrow search

Year of publication
Subject
All
genetic algorithms 5 learning classifier systems 4 product positioning 2 Bounded Rationality 1 Industrial Engineering 1 Innovation 1 Learning 1 Learning Classifier Systems 1 Models and algorithms for effective decision-making in a data-driven environment are discussed. To enhance the quality of the extracted knowledge and decision-making 1 Sustainability 1 Technical Progress 1 a subset of rules is selected from the extracted knowledge to meet the established decision-making criteria. The parameter values represented by the conditions of this set of rules are called a decision signature. A model and algorithms for the selection of the desired parameters (decision signatures) will be developed. The parameters of this model are updated using a framework provided by the learning classifier systems 1 adaptive systems modelling 1 agent based modelling 1 and the parameters optimizing process performance are recommended. The applications discussed in this paper differ from most data mining tasks 1 artificial adaptive agents 1 game theory 1 genetic-based learning classifier systems 1 heterogeneous agents 1 imitating 1 imperfect information 1 in a typical data mining application the equipment fault is recognized based on the failure symptoms. In this paper 1 information flow 1 knowledge 1 learning 1 market simulation 1 new product development 1 organisational learning 1 quality function deployment 1 stock returns forecasting 1 the data sets are transformed 1 the impact of the decisions on the modeled process is simulated 1 the knowledge is extracted with multiple algorithms 1 total quality management 1 where the extracted knowledge is used to assign decision values to new objects that have not been included in the training data. For example 1
more ... less ...
Online availability
All
Free 5
Type of publication
All
Book / Working Paper 5 Article 1 Other 1
Language
All
Undetermined 7
Author
All
Fent, Thomas 4 Galimberti, Jaqueson K. 1 Kusiak, Andrew 1 Silva, Sergio da 1 Yildizoglu, Murat 1
Institution
All
Volkswirtschaftliche Fakultät, Ludwig-Maximilians-Universität München 3 Groupe de Recherche en Économie Théorique et Appliquée (GREThA), Université de Bordeaux 1 Society for Computational Economics - SCE 1
Published in...
All
MPRA Paper 3 Economics Bulletin 1 Modeling, Computing, and Mastering Complexity 2003 1 Working Papers / Groupe de Recherche en Économie Théorique et Appliquée (GREThA), Université de Bordeaux 1
Source
All
RePEc 6 BASE 1
Showing 1 - 7 of 7
Cover Image
An empirical case against the use of genetic-based learning classifier systems as forecasting devices
Galimberti, Jaqueson K.; Silva, Sergio da - In: Economics Bulletin 32 (2012) 1, pp. 354-369
classifier systems to represent agents process of expectations formation, an approach commonly found into the agent … walk in forecasting stock returns. We then argue that our results cast doubts on the plausibility of using learning …
Persistent link: https://www.econbiz.de/10011278532
Saved in:
Cover Image
Data mining and decision making
Kusiak, Andrew - 2002
provided by the learning classifier systems. …
Persistent link: https://www.econbiz.de/10009466041
Saved in:
Cover Image
Wissen gewinnen und gewinnen durch Wissen
Fent, Thomas - Volkswirtschaftliche Fakultät, … - 2000
According to Alfred Korzybski (1921) humans unlike plants and animals have the property to bind time, i.e. they are able to transfer experience through time. Humans are capable to collenct knowledge from the past and communicate their knowledge to the future. This paper investigates the...
Persistent link: https://www.econbiz.de/10005623357
Saved in:
Cover Image
Adaptive agents in the House of Quality
Fent, Thomas - Volkswirtschaftliche Fakultät, … - 1999
Managing the information flow within a big organization is a challenging task. Moreover, in a distributed decision-making process conflicting objectives occur. In this paper, artificial adaptive agents are used to analyze this problem. The decision makers are implemented as Classifier Systems,...
Persistent link: https://www.econbiz.de/10005623274
Saved in:
Cover Image
Using Genetics Based Machine Learning to find Strategies for Product Placement in a dynamic Market
Fent, Thomas - Volkswirtschaftliche Fakultät, … - 1999
In this paper we discuss the necessity of models including complex adaptive systems in order to eliminate the shortcomings of neoclassical models based on equilibrium theory. A simulation model containing artificial adaptive agents is used to explore the dynamics of a market of highly...
Persistent link: https://www.econbiz.de/10005789342
Saved in:
Cover Image
Modeling Adaptive Learning: R&D Strategies in the Model of Nelson & Winter (1982)
Yildizoglu, Murat - Groupe de Recherche en Économie Théorique et … - 2001
This article aims to test the relevance of learning through Genetic Algorithms (GA) and Learning Classifier Systems …
Persistent link: https://www.econbiz.de/10005729424
Saved in:
Cover Image
MACRO AND MICRO DYNAMICS IN AN ARTIFICIAL SOCIETY: AN AGENT BASED APPROACH
Fent, Thomas - Society for Computational Economics - SCE
This paper deals with artificial agents buying and selling products in a virtual market of goods that may be substituted for each other. On the demand side the market features a homogenous group of agents whose dynamics are determined by three different scenarios. The supply side, on the other...
Persistent link: https://www.econbiz.de/10005345746
Saved in:
A service of the
zbw
  • Sitemap
  • Plain language
  • Accessibility
  • Contact us
  • Imprint
  • Privacy

Loading...