Applying machine learning to agricultural data
References (36)
PRISM: an algorithm for inducing modular rules
Int. J. Man Mach. Stud.
(1987)- et al.
AUTOCLASS: a Bayesian classification system
- et al.
Models of incremental concept formation
Artif. Intell.
(1989) A theory and methodology of inductive learning
Artif. Intell.
(1983)- et al.
Using concept learning for knowledge acquisition
Int. J. Man Mach. Stud.
(1988) - et al.
Inductive inference: theory and methods
Comput. Surv.
(1983) - et al.
PROTOS: an exemplar-based learning apprentice
Int. J. Man Mach. Stud.
(1988) - et al.
Classification and regression trees
(1984) - et al.
Avoiding pitfalls when learning recursive theories
- et al.
Explanation-based learning: an alternative view
Mach. Learn.
(1986)
Knowledge acquisition via incremental conceptual clustering
Mach. Learn.
The trade-off between knowledge and data in knowledge acquisition
A survey of clustering methods
Learning conjunctive concepts in structural domains
EMERALD 2: an integrated system of machine learning and discovery programs to support education and experimental research
Building a machine learning toolbox
MLC++: a machine learning library in C++
Concept learning in a rich input domain: generalization-based memory
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