Belief Functions: Theory and Applications - Lecture Notes in Computer Science
Latest Publications

TOTAL DOCUMENTS

(FIVE YEARS 0)

H-INDEX

(FIVE YEARS 0)

Published By Springer International Publishing

9783319993829, 9783319993836

Latest Documents Most Cited Documents Contributed Authors Related Sources Related Keywords

Linear Belief Functions for Data Analytics

Belief Functions: Theory and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-319-99383-6_23 ◽

2018 ◽

pp. 181-189

Author(s):

Liping Liu

Keyword(s):

Data Analytics ◽

Belief Functions

Improved Performance of EK-NNClus by Selecting Appropriate Parameter

Belief Functions: Theory and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-319-99383-6_31 ◽

2018 ◽

pp. 252-259

Author(s):

Qian Wang ◽

Zhi-gang Su

Keyword(s):

Improved Performance

Prejudiced Information Fusion Using Belief Functions

Belief Functions: Theory and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-319-99383-6_11 ◽

2018 ◽

pp. 77-85 ◽

Cited By ~ 1

Author(s):

Didier Dubois ◽

Francis Faux ◽

Henri Prade

Keyword(s):

Information Fusion ◽

Belief Functions

From Relations Between Sets to Relations Between Belief Functions

Belief Functions: Theory and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-319-99383-6_9 ◽

2018 ◽

pp. 65-72

Author(s):

Sébastien Destercke ◽

Frédéric Pichon ◽

John Klein

Keyword(s):

Belief Functions

A Compact Belief Rule-Based Classification System with Evidential Clustering

Belief Functions: Theory and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-319-99383-6_18 ◽

2018 ◽

pp. 137-145

Author(s):

Lianmeng Jiao ◽

Xiaojiao Geng ◽

Quan Pan

Keyword(s):

Classification System ◽

Rule Based

General Geometry of Belief Function Combination

Belief Functions: Theory and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-319-99383-6_7 ◽

2018 ◽

pp. 48-56

Author(s):

Fabio Cuzzolin

Keyword(s):

Belief Function ◽

Function Combination ◽

General Geometry

An Evidential K-Nearest Neighbor Classifier Based on Contextual Discounting and Likelihood Maximization

Belief Functions: Theory and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-319-99383-6_20 ◽

2018 ◽

pp. 155-162 ◽

Cited By ~ 2

Author(s):

Orakanya Kanjanatarakul ◽

Siwarat Kuson ◽

Thierry Denoeux

Keyword(s):

Nearest Neighbor ◽

K Nearest Neighbor ◽

Nearest Neighbor Classifier ◽

Neighbor Classifier

An Evidential Collaborative Filtering Approach Based on Items Contents Clustering

Belief Functions: Theory and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-319-99383-6_1 ◽

2018 ◽

pp. 1-9

Author(s):

Raoua Abdelkhalek ◽

Imen Boukhris ◽

Zied Elouedi

Keyword(s):

Collaborative Filtering ◽

Filtering Approach

On the Conflict Measures Agreed with the Combining Rules

Belief Functions: Theory and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-319-99383-6_22 ◽

2018 ◽

pp. 172-180 ◽

Cited By ~ 1

Author(s):

Alexander Lepskiy

Study of Distributed Data Fusion Using Dempster’s Rule and Cautious Operator

Belief Functions: Theory and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-319-99383-6_13 ◽

2018 ◽

pp. 95-102

Author(s):

Romain Guyard ◽

Véronique Cherfaoui

Keyword(s):

Data Fusion ◽

Distributed Data ◽

Distributed Data Fusion

Belief Functions: Theory and Applications - Lecture Notes in Computer ScienceLatest Publications

TOTAL DOCUMENTS

H-INDEX

Published By Springer International Publishing

Linear Belief Functions for Data Analytics

Improved Performance of EK-NNClus by Selecting Appropriate Parameter

Prejudiced Information Fusion Using Belief Functions

From Relations Between Sets to Relations Between Belief Functions

A Compact Belief Rule-Based Classification System with Evidential Clustering

General Geometry of Belief Function Combination

An Evidential K-Nearest Neighbor Classifier Based on Contextual Discounting and Likelihood Maximization

An Evidential Collaborative Filtering Approach Based on Items Contents Clustering

On the Conflict Measures Agreed with the Combining Rules

Study of Distributed Data Fusion Using Dempster’s Rule and Cautious Operator

Belief Functions: Theory and Applications - Lecture Notes in Computer Science
Latest Publications