Applied multivariate statistical analysis
Applied multivariate statistical analysis
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Instance-Based Learning Algorithms
Machine Learning
Detecting novel classes with applications to fault diagnosis
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Algorithmic learning
Machine Learning
IC2: an interval based characteristic concept learner
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
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It is argued that in applications of concept learning from examples where not every possible category of the domain is present in the training set (i.e., most real world applications), classification performance can be improved by integrating suitable discriminative and characteristic classification schemes. The suggested approach is to first discriminate between the categories present in the training set and then characterize each of these categories against all possible categories. To show the viability of this approach, a number of different discriminators and characterizers are integrated and tested. In particular, a novel characterization method that makes use of the information about the statistical distribution of feature values that can be extracted from the training examples is used. The experimental results strongly supports the thesis of the paper.