A methodology of design knowledge acquisition for use in learning expert systems
International Journal of Man-Machine Studies
Design knowledge acquisition: task analysis and a partial implementation
Knowledge Acquisition
C4.5: programs for machine learning
C4.5: programs for machine learning
Measuring the value of knowledge
International Journal of Human-Computer Studies
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Applications of machine learning and rule induction
Communications of the ACM
Applying classification algorithms in practice
Statistics and Computing
Machine Learning
Explanation-Based Generalization: A Unifying View
Machine Learning
Constructing and Sharing Perceptual Distiinctions
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Characterization of Classification Algorithms
EPIA '95 Proceedings of the 7th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
A data mining-based engineering design support system: a research agenda
Data mining for design and manufacturing
Ensemble modelling or selecting the best model: Many could be better than one
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Modeling collective learning in design
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
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The application of machine learning (ML) to solve practical problems is complex. Only recently, due to the increased promise of ML in solving real problems and the experienced difficulty of their use, has this issue started to attract attention. This difficulty arises from the complexity of learning problems and the large variety of available techniques. In order to understand this complexity and begin to overcome it, it is important to construct a characterization of learning situations. Building on previous work that dealt with the practical use of ML, a set of dimensions is developed, contrasted with another recent proposal, and illustrated with a project on the development of a decision-support system for marine propeller design. The general research opportunities that emerge from the development of the dimensions are discussed. Leading toward working systems, a simple model is presented for setting priorities in research and in selecting learning tasks within large projects. Central to the development of the concepts discussed in this paper is their use in future projects and the recording of their successes, limitations, and failures.