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The Nature of Statistical Learning Theory
, 1999
"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."
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Cited by 12976 (32 self)
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Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based
The DempsterShafer calculus for statisticians
 International Journal of Approximate Reasoning
, 2007
"... The DempsterShafer (DS) theory of probabilistic reasoning is presented in terms of a semantics whereby every meaningful formal assertion is associated with a triple (p, q, r) where p is the probability “for ” the assertion, q is the probability “against” the assertion, and r is the probability of “ ..."
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Cited by 46 (1 self)
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The DempsterShafer (DS) theory of probabilistic reasoning is presented in terms of a semantics whereby every meaningful formal assertion is associated with a triple (p, q, r) where p is the probability “for ” the assertion, q is the probability “against” the assertion, and r is the probability
Algorithms for dempstershafer theory
 Algorithms for Uncertainty and Defeasible Reasoning
, 2000
"... The method of reasoning with uncertain information known as DempsterShafer theory arose from the reinterpretation and development of work of Arthur Dempster [Dempster, 67; 68] by Glenn Shafer in his book a mathematical theory of evidence [Shafer, 76], and further publications e.g., [Shafer, 81; 90] ..."
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Cited by 20 (3 self)
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The method of reasoning with uncertain information known as DempsterShafer theory arose from the reinterpretation and development of work of Arthur Dempster [Dempster, 67; 68] by Glenn Shafer in his book a mathematical theory of evidence [Shafer, 76], and further publications e.g., [Shafer, 81; 90
DempsterShafer Argument Schemes
"... Abstract. DempsterShafer theory, which can be regarded as a generalisation of probability theory, is a widely used formalism for reasoning with uncertain information. The application of the theory hinges on the use of a rule for combining evidence from different sources. A number of different comb ..."
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Abstract. DempsterShafer theory, which can be regarded as a generalisation of probability theory, is a widely used formalism for reasoning with uncertain information. The application of the theory hinges on the use of a rule for combining evidence from different sources. A number of different
Creating Prototypes for Fast Classification in DempsterShafer Clustering
 Qualitative and Quantitative Practical Reasoning, D. M. Gabbay, R. Kruse, A. Nonnengart, H. J. Ohlbach (Eds.), Proceedings of the First International Joint Conference (ECSQARUFAPR'97)
, 1997
"... We develop a classification method for incoming pieces of evidence in DempsterShafer theory. This methodology is based on previous work with clustering and specification of originally nonspecific evidence. This methodology is here put in order for fast classification of future incoming pieces of ev ..."
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Cited by 9 (9 self)
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We develop a classification method for incoming pieces of evidence in DempsterShafer theory. This methodology is based on previous work with clustering and specification of originally nonspecific evidence. This methodology is here put in order for fast classification of future incoming pieces
DempsterShafer theory of evidence
, 1994
"... comprehensive comparison between generalized incidence calculus and the ..."
Integrating classification and association rule mining
 In Proc of KDD
, 1998
"... Classification rule mining aims to discover a small set of rules in the database that forms an accurate classifier. Association rule mining finds all the rules existing in the database that satisfy some minimum support and minimum confidence constraints. For association rule mining, the target of di ..."
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Cited by 561 (21 self)
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Classification rule mining aims to discover a small set of rules in the database that forms an accurate classifier. Association rule mining finds all the rules existing in the database that satisfy some minimum support and minimum confidence constraints. For association rule mining, the target
Landcover classification in MRF context using DempsterShafer fusion for multisensor imagery
 IEEE TRANS. IMAGE PROCESS
, 2005
"... This work deals with multisensor data fusion to obtain landcover classification. The role of featurelevel fusion using the Dempster–Shafer rule and that of datalevel fusion in the MRF context is studied in this paper to obtain an optimally segmented image. Subsequently, segments are validated and ..."
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Cited by 2 (0 self)
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This work deals with multisensor data fusion to obtain landcover classification. The role of featurelevel fusion using the Dempster–Shafer rule and that of datalevel fusion in the MRF context is studied in this paper to obtain an optimally segmented image. Subsequently, segments are validated
Very simple classification rules perform well on most commonly used datasets
 Machine Learning
, 1993
"... The classification rules induced by machine learning systems are judged by two criteria: their classification accuracy on an independent test set (henceforth "accuracy"), and their complexity. The relationship between these two criteria is, of course, of keen interest to the machin ..."
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Cited by 542 (5 self)
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The classification rules induced by machine learning systems are judged by two criteria: their classification accuracy on an independent test set (henceforth "accuracy"), and their complexity. The relationship between these two criteria is, of course, of keen interest
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