Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Instance-Based Learning Algorithms
Machine Learning
A method for inductive cost optimization
EWSL-91 Proceedings of the European working session on learning on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Software engineering (4th ed.)
Software engineering (4th ed.)
Case-based reasoning
Towards a technology and a science of machine learning
AI Communications
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Applications of machine learning and rule induction
Communications of the ACM
Genetic algorithms in machine learning
AI Communications
AI Communications
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Overcoming Process Delays with Decision Tree Induction
IEEE Expert: Intelligent Systems and Their Applications
Machine Learning
Machine Learning
ENIGMA: A System That Learns Diagnostic Knowledge
IEEE Transactions on Knowledge and Data Engineering
Machine Learning and Its Applications, Advanced Lectures
Applications of machine learning: matching problems to tasks and methods
The Knowledge Engineering Review
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Interest for machine learning techniques has increased over the last decade. In spite of this the application practice of these techniques has never been systematically analysed. This paper analyses the practical application of Inductive Learning Techniques in the Netherlands by means of a survey. Results of this survey are assessed in terms of introduction of (information) technological innovation. The application practice for these techniques finds itself in an initial stage. Current practice is dominated by technical issues and there is little attention for methodological issues associated with analysis of the problem and data collection. In the paper we propose a four level model for describing the methodological aspects of ILT application. The current practice of application concentrates on the two lowermost layers. Tools for developing applications concentrate mainly on the lowest, technical level.