Why functional programming matters
The Computer Journal - Special issue on Lazy functional programming
Fast text searching: allowing errors
Communications of the ACM
Applications of machine learning and rule induction
Communications of the ACM
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Alphabet indexing for approximating features of symbols
Theoretical Computer Science - Special issue: Genome informatics
The Definition of Standard ML
Machine Learning
VM lambda: A Functional Calculusfor Scientific Discovery
FLOPS '02 Proceedings of the 6th International Symposium on Functional and Logic Programming
Views: Fundamental Building Blocks in the Process of Knowledge Discovery
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
Toward Genomic Hypothesis Creator: View Designer for Discovery
DS '98 Proceedings of the First International Conference on Discovery Science
The Computer-Aided Discovery of Scientific Knowledge
DS '98 Proceedings of the First International Conference on Discovery Science
Designing Views in HypothesisCreator: System for Assisting in Discovery
DS '99 Proceedings of the Second International Conference on Discovery Science
VM lambda: A Functional Calculusfor Scientific Discovery
FLOPS '02 Proceedings of the 6th International Symposium on Functional and Logic Programming
Foundations of Designing Computational Knowledge Discovery Processes
Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
An interactive environment for scientific model construction
Proceedings of the 2nd international conference on Knowledge capture
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We present the concept of a functional programming language called VML (View Modeling Language), providing facilities to increase the efficiency of the iterative, trial-and-error cycle which frequently appears in any knowledge discovery process. In VML, functions can be specified so that returning values implicitly "remember", with a special internal representation, that it was calculated from the corresponding function. VML also provides facilities for "matching" the remembered representation so that one can easily obtain, from a given value, the functions and/or parameters used to create the value. Further, we describe, as VML programs, successful knowledge discovery tasks which we have actually experienced in the biological domain, and argue that computational knowledge discovery experiments can be efficiently developed and conducted using this language.