Spatial Consistency in 3D Tract-Based Clustering Statistics
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Agents learn from human experts: an approach to test reconfigurable systems
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Design of an automatic wood types classification system by using fluorescence spectra
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Inductive learning methods allow the system designer to infer a model of the relevant phenomena of an unknown process by extracting information from experimental data. A wide range of inductive learning methods is nowadays available, potentially ensuring different levels of accuracy on different problem domains. In this critical review of theoretic results gained in the last decade, we address the problem of designing an inductive classification system with optimal accuracy when domain knowledge is limited and the number of available experiments is-possibly-small. By analyzing the formal properties of consistent learning methods and of accuracy estimators, we wish to convey to the reader the message that the common practice of aggressively pursuing error minimization with different training algorithms and classification families is unjustified