Neural networks for object classification in a knowledge representation model

被引:0
|
作者
Bassolet, CG
Simonet, A
Simonet, M
机构
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The Osiris system implements an objet data model wich was designed to enable users to share knowledge through a view mechanism. Views, wich play the role os subclasses in an object perspective, are defined by their logical properties. Determining the views whose properties are satisfied by a given instance is the very mechanism of classification in knowledge bases. A in-depth static analysis of the views and their logical properties allows the construction at compile time of a network to classify the objects of the base.
引用
收藏
页码:145 / 155
页数:11
相关论文
共 50 条
  • [1] An abstract representation of geometric knowledge for object classification
    Sangineto, E
    [J]. PATTERN RECOGNITION LETTERS, 2003, 24 (9-10) : 1241 - 1250
  • [2] Neural Networks and Structured Knowledge: Knowledge Representation and Reasoning
    Franz J. Kurfess
    [J]. Applied Intelligence, 1999, 11 : 5 - 13
  • [3] Neural networks and structured knowledge: Knowledge representation and reasoning
    Kurfess, FJ
    [J]. APPLIED INTELLIGENCE, 1999, 11 (01) : 5 - 13
  • [4] On Representation Knowledge Distillation for Graph Neural Networks
    Joshi, Chaitanya K.
    Liu, Fayao
    Xun, Xu
    Lin, Jie
    Foo, Chuan Sheng
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2024, 35 (04) : 4656 - 4667
  • [5] Knowledge representation and possible worlds for neural networks
    Healy, Michael J.
    Caudell, Thomas P.
    [J]. 2006 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORK PROCEEDINGS, VOLS 1-10, 2006, : 3047 - +
  • [6] A Shared Representation for Object Tracking and Classification using Siamese Networks
    Kretz, Adrian
    Mester, Rudolf
    [J]. 2020 IEEE SOUTHWEST SYMPOSIUM ON IMAGE ANALYSIS AND INTERPRETATION (SSIAI 2020), 2020, : 54 - 57
  • [7] Conceptual representation in the object classification task:: verification of the model
    Plichtová, J
    [J]. CESKOSLOVENSKA PSYCHOLOGIE, 1998, 42 (05): : 407 - 428
  • [8] Faint object classification using artificial neural networks
    SerraRicart, M
    Gaitan, V
    Garrido, L
    PerezFournon, I
    [J]. ASTRONOMY & ASTROPHYSICS SUPPLEMENT SERIES, 1996, 115 (01): : 195 - 207
  • [9] Hierarchical classification of object images using neural networks
    Kim, Jong-Ho
    Lee, Jae-Won
    Kang, Byoung-Doo
    Kwon, O-Hwa
    Seong, Chi-Young
    Kim, Sang-Kyoon
    Park, Se-Myung
    [J]. ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 2, PROCEEDINGS, 2006, 3972 : 322 - 330
  • [10] Natural object classification using artificial neural networks
    Singh, S
    Markou, M
    Haddon, J
    [J]. IJCNN 2000: PROCEEDINGS OF THE IEEE-INNS-ENNS INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOL III, 2000, : 139 - 144