Connectionist learning procedures
Artificial Intelligence
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Theory of T-norms and fuzzy inference methods
Fuzzy Sets and Systems - Special memorial volume on fuzzy logic and uncertainly modelling
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Pattern classification: a unified view of statistical and neural approaches
Pattern classification: a unified view of statistical and neural approaches
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
An analysis of Bayesian classifiers
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
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We define a generalized likelihood function based on uncertainty measures and show that maximizing such a likelihood function for different measures induces different types of classifiers. In the probabilistic framework, we obtain classifiers that optimize the cross-entropy function. In the possibilistic framework, we obtain classifiers that maximize the interclass margin. Furthermore, we show that the support vector machine is a sub-class of these maximummargin classifiers.