Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: concepts and techniques
Data mining: concepts and techniques
Machine Learning
Machine Learning
A Small Set of Formal Topological Relationships Suitable for End-User Interaction
SSD '93 Proceedings of the Third International Symposium on Advances in Spatial Databases
Classification in geographical information systems
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
DrC4.5: Improving C4.5 by means of prior knowledge
Proceedings of the 2005 ACM symposium on Applied computing
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The use of classification trees in two quite different application areas –business documents on one side and geographic information systems on the other– is presented. What is in common between such so different applications of the classification techniques based on trees is the need of complementing the straightforward use of induction with the exploitation of some form of deductive, or better to say expert, knowledge. When working on business documents, the expert knowledge, in the form of rules elicited from human experts, is used to improve the construction of the classification tree by complementing the inductive knowledge coming from the examples in the choice of the next node to add to the tree. When working on geographic information systems, the expert knowledge, in the form of specifying which are the spatial relationships among the geographic objects, is used to extract the information from the GIS in a form that can be then processed in an inductive style.