The Combination of Evidence in the Transferable Belief Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear regression with stable disturbances
A practical guide to heavy tails
Estimation of the Bivariate Stable Spectral Representation by theProjection Method
Computational Economics - Special issue on computational studies at Cambridge
Skewed α-stable distributions for modelling textures
Pattern Recognition Letters
Constructing the Pignistic Probability Function in a Context of Uncertainty
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
International Journal of Approximate Reasoning
Bayesian data fusion of multiview synthetic aperture sonar iagery for seabed classification
IEEE Transactions on Image Processing
Belief functions on real numbers
International Journal of Approximate Reasoning
Target identification based on the transferable belief model interpretation of dempster-shafer model
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Estimation of stable spectral measures
Mathematical and Computer Modelling: An International Journal
Adaptive target detection in foliage-penetrating SAR images using alpha-stable models
IEEE Transactions on Image Processing
Hi-index | 0.00 |
The aim of this paper is to show the interest in fitting features with an @a-stable distribution to classify imperfect data. The supervised pattern recognition is thus based on the theory of continuous belief functions, which is a way to consider imprecision and uncertainty of data. The distributions of features are supposed to be unimodal and estimated by a single Gaussian and @a-stable model. Experimental results are first obtained from synthetic data by combining two features of one dimension and by considering a vector of two features. Mass functions are calculated from plausibility functions by using the generalized Bayes theorem. The same study is applied to the automatic classification of three types of sea floor (rock, silt and sand) with features acquired by a mono-beam echo-sounder. We evaluate the quality of the @a-stable model and the Gaussian model by analyzing qualitative results, using a Kolmogorov-Smirnov test (K-S test), and quantitative results with classification rates. The performances of the belief classifier are compared with a Bayesian approach.