Knowledge extraction from unsupervised multi-topographic neural network models
Identifieur interne :
000552 ( PascalFrancis/Curation );
précédent :
000551;
suivant :
000553
Knowledge extraction from unsupervised multi-topographic neural network models
Auteurs : Shadi Al Shehabi [
France] ;
Jean-Charles Lamirel [
France]
Source :
-
Lecture notes in computer science [ 0302-9743 ] ; 2005.
RBID : Pascal:06-0067861
Descripteurs français
- Pascal (Inist)
- Méthode formelle,
Intelligence artificielle,
Découverte connaissance,
Apprentissage non supervisé,
Fouille donnée,
Système expert,
Contrôle qualité,
Classification,
Base donnée,
Association statistique,
Brevet,
Propriété industrielle,
Réseau neuronal,
Modélisation,
Base connaissance,
Treillis Galois,
Règle association.
- Wicri :
English descriptors
- KwdEn :
- Artificial intelligence,
Association rule,
Classification,
Data mining,
Database,
Expert system,
Formal method,
Galois lattice,
Knowledge base,
Knowledge discovery,
Modeling,
Neural network,
Patent rights,
Patents,
Quality control,
Statistical association,
Unsupervised learning.
Abstract
This paper presents a new approach whose aim is to extent the scope of numerical models by providing them with knowledge extraction capabilities. The basic model which is considered in this paper is a multi-topographic neural network model. One of the most powerful features of this model is its generalization mechanism that allows rule extraction to be performed. The extraction of association rules is itself based on original quality measures which evaluate to what extent a numerical classification model behaves as a natural symbolic classifier such as a Galois lattice. A first experimental illustration of rule extraction on documentary data constituted by a set of patents issued form a patent database is presented.
| pA |
| A01 | 01 | 1 | | @0 0302-9743 |
|---|
| A05 | | | | @2 3697 |
|---|
| A08 | 01 | 1 | ENG | @1 Knowledge extraction from unsupervised multi-topographic neural network models |
|---|
| A09 | 01 | 1 | ENG | @1 Artificial neural networks. Part II : formal models and their applications : ICANN 2005 : 15th International Conference, Warsaw, Poland, September 11-15, 2005 : proceedings |
|---|
| A11 | 01 | 1 | | @1 AL SHEHABI (Shadi) |
|---|
| A11 | 02 | 1 | | @1 LAMIREL (Jean-Charles) |
|---|
| A14 | 01 | | | @1 Loria, Campus Scientifique, BP 239 @2 54506 Vandoeuvre-lès-Nancy @3 FRA @Z 1 aut. @Z 2 aut. |
|---|
| A20 | | | | @1 479-484 |
|---|
| A21 | | | | @1 2005 |
|---|
| A23 | 01 | | | @0 ENG |
|---|
| A26 | 01 | | | @0 3-540-28755-8 |
|---|
| A43 | 01 | | | @1 INIST @2 16343 @5 354000138682610750 |
|---|
| A44 | | | | @0 0000 @1 © 2006 INIST-CNRS. All rights reserved. |
|---|
| A45 | | | | @0 12 ref. |
|---|
| A47 | 01 | 1 | | @0 06-0067861 |
|---|
| A60 | | | | @1 P @2 C |
|---|
| A61 | | | | @0 A |
|---|
| A64 | 01 | 1 | | @0 Lecture notes in computer science |
|---|
| A66 | 01 | | | @0 DEU |
|---|
| C01 | 01 | | ENG | @0 This paper presents a new approach whose aim is to extent the scope of numerical models by providing them with knowledge extraction capabilities. The basic model which is considered in this paper is a multi-topographic neural network model. One of the most powerful features of this model is its generalization mechanism that allows rule extraction to be performed. The extraction of association rules is itself based on original quality measures which evaluate to what extent a numerical classification model behaves as a natural symbolic classifier such as a Galois lattice. A first experimental illustration of rule extraction on documentary data constituted by a set of patents issued form a patent database is presented. |
|---|
| C02 | 01 | X | | @0 001D02C |
|---|
| C03 | 01 | X | FRE | @0 Méthode formelle @5 01 |
|---|
| C03 | 01 | X | ENG | @0 Formal method @5 01 |
|---|
| C03 | 01 | X | SPA | @0 Método formal @5 01 |
|---|
| C03 | 02 | X | FRE | @0 Intelligence artificielle @5 02 |
|---|
| C03 | 02 | X | ENG | @0 Artificial intelligence @5 02 |
|---|
| C03 | 02 | X | SPA | @0 Inteligencia artificial @5 02 |
|---|
| C03 | 03 | X | FRE | @0 Découverte connaissance @5 06 |
|---|
| C03 | 03 | X | ENG | @0 Knowledge discovery @5 06 |
|---|
| C03 | 03 | X | SPA | @0 Descubrimiento conocimiento @5 06 |
|---|
| C03 | 04 | 3 | FRE | @0 Apprentissage non supervisé @5 07 |
|---|
| C03 | 04 | 3 | ENG | @0 Unsupervised learning @5 07 |
|---|
| C03 | 05 | X | FRE | @0 Fouille donnée @5 08 |
|---|
| C03 | 05 | X | ENG | @0 Data mining @5 08 |
|---|
| C03 | 05 | X | SPA | @0 Busca dato @5 08 |
|---|
| C03 | 06 | X | FRE | @0 Système expert @5 09 |
|---|
| C03 | 06 | X | ENG | @0 Expert system @5 09 |
|---|
| C03 | 06 | X | SPA | @0 Sistema experto @5 09 |
|---|
| C03 | 07 | X | FRE | @0 Contrôle qualité @5 10 |
|---|
| C03 | 07 | X | ENG | @0 Quality control @5 10 |
|---|
| C03 | 07 | X | SPA | @0 Control calidad @5 10 |
|---|
| C03 | 08 | X | FRE | @0 Classification @5 11 |
|---|
| C03 | 08 | X | ENG | @0 Classification @5 11 |
|---|
| C03 | 08 | X | SPA | @0 Clasificación @5 11 |
|---|
| C03 | 09 | X | FRE | @0 Base donnée @5 12 |
|---|
| C03 | 09 | X | ENG | @0 Database @5 12 |
|---|
| C03 | 09 | X | SPA | @0 Base dato @5 12 |
|---|
| C03 | 10 | X | FRE | @0 Association statistique @5 18 |
|---|
| C03 | 10 | X | ENG | @0 Statistical association @5 18 |
|---|
| C03 | 10 | X | SPA | @0 Asociación estadística @5 18 |
|---|
| C03 | 11 | X | FRE | @0 Brevet @5 19 |
|---|
| C03 | 11 | X | ENG | @0 Patents @5 19 |
|---|
| C03 | 11 | X | SPA | @0 Patente @5 19 |
|---|
| C03 | 12 | X | FRE | @0 Propriété industrielle @5 20 |
|---|
| C03 | 12 | X | ENG | @0 Patent rights @5 20 |
|---|
| C03 | 12 | X | SPA | @0 Propiedad industrial @5 20 |
|---|
| C03 | 13 | X | FRE | @0 Réseau neuronal @5 23 |
|---|
| C03 | 13 | X | ENG | @0 Neural network @5 23 |
|---|
| C03 | 13 | X | SPA | @0 Red neuronal @5 23 |
|---|
| C03 | 14 | X | FRE | @0 Modélisation @5 24 |
|---|
| C03 | 14 | X | ENG | @0 Modeling @5 24 |
|---|
| C03 | 14 | X | SPA | @0 Modelización @5 24 |
|---|
| C03 | 15 | X | FRE | @0 Base connaissance @5 25 |
|---|
| C03 | 15 | X | ENG | @0 Knowledge base @5 25 |
|---|
| C03 | 15 | X | SPA | @0 Base conocimiento @5 25 |
|---|
| C03 | 16 | X | FRE | @0 Treillis Galois @5 26 |
|---|
| C03 | 16 | X | ENG | @0 Galois lattice @5 26 |
|---|
| C03 | 16 | X | SPA | @0 Retículo Galois @5 26 |
|---|
| C03 | 17 | X | FRE | @0 Règle association @4 CD @5 96 |
|---|
| C03 | 17 | X | ENG | @0 Association rule @4 CD @5 96 |
|---|
| C03 | 17 | X | SPA | @0 Regla asociación @4 CD @5 96 |
|---|
| N21 | | | | @1 037 |
|---|
| N44 | 01 | | | @1 OTO |
|---|
| N82 | | | | @1 OTO |
|---|
|
|---|
| pR |
| A30 | 01 | 1 | ENG | @1 International Conference on Artificial Neural Networks @2 15 @3 Warsaw POL @4 2005-09-11 |
|---|
|
|---|
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Le document en format XML
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<term>Propriété industrielle</term>
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</keywords><front><div type="abstract" xml:lang="en">This paper presents a new approach whose aim is to extent the scope of numerical models by providing them with knowledge extraction capabilities. The basic model which is considered in this paper is a multi-topographic neural network model. One of the most powerful features of this model is its generalization mechanism that allows rule extraction to be performed. The extraction of association rules is itself based on original quality measures which evaluate to what extent a numerical classification model behaves as a natural symbolic classifier such as a Galois lattice. A first experimental illustration of rule extraction on documentary data constituted by a set of patents issued form a patent database is presented.</div>
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</fA01><fA05><s2>3697</s2>
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</fA08><fA09 i1="01" i2="1" l="ENG"><s1>Artificial neural networks. Part II : formal models and their applications : ICANN 2005 : 15th International Conference, Warsaw, Poland, September 11-15, 2005 : proceedings</s1>
</fA09><fA11 i1="01" i2="1"><s1>AL SHEHABI (Shadi)</s1>
</fA11><fA11 i1="02" i2="1"><s1>LAMIREL (Jean-Charles)</s1>
</fA11><fA14 i1="01"><s1>Loria, Campus Scientifique, BP 239</s1>
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<sZ>2 aut.</sZ>
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<s2>16343</s2>
<s5>354000138682610750</s5>
</fA43><fA44><s0>0000</s0>
<s1>© 2006 INIST-CNRS. All rights reserved.</s1>
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</fA45><fA47 i1="01" i2="1"><s0>06-0067861</s0>
</fA47><fA60><s1>P</s1>
<s2>C</s2>
</fA60><fA64 i1="01" i2="1"><s0>Lecture notes in computer science</s0>
</fA64><fA66 i1="01"><s0>DEU</s0>
</fA66><fC01 i1="01" l="ENG"><s0>This paper presents a new approach whose aim is to extent the scope of numerical models by providing them with knowledge extraction capabilities. The basic model which is considered in this paper is a multi-topographic neural network model. One of the most powerful features of this model is its generalization mechanism that allows rule extraction to be performed. The extraction of association rules is itself based on original quality measures which evaluate to what extent a numerical classification model behaves as a natural symbolic classifier such as a Galois lattice. A first experimental illustration of rule extraction on documentary data constituted by a set of patents issued form a patent database is presented.</s0>
</fC01><fC02 i1="01" i2="X"><s0>001D02C</s0>
</fC02><fC03 i1="01" i2="X" l="FRE"><s0>Méthode formelle</s0>
<s5>01</s5>
</fC03><fC03 i1="01" i2="X" l="ENG"><s0>Formal method</s0>
<s5>01</s5>
</fC03><fC03 i1="01" i2="X" l="SPA"><s0>Método formal</s0>
<s5>01</s5>
</fC03><fC03 i1="02" i2="X" l="FRE"><s0>Intelligence artificielle</s0>
<s5>02</s5>
</fC03><fC03 i1="02" i2="X" l="ENG"><s0>Artificial intelligence</s0>
<s5>02</s5>
</fC03><fC03 i1="02" i2="X" l="SPA"><s0>Inteligencia artificial</s0>
<s5>02</s5>
</fC03><fC03 i1="03" i2="X" l="FRE"><s0>Découverte connaissance</s0>
<s5>06</s5>
</fC03><fC03 i1="03" i2="X" l="ENG"><s0>Knowledge discovery</s0>
<s5>06</s5>
</fC03><fC03 i1="03" i2="X" l="SPA"><s0>Descubrimiento conocimiento</s0>
<s5>06</s5>
</fC03><fC03 i1="04" i2="3" l="FRE"><s0>Apprentissage non supervisé</s0>
<s5>07</s5>
</fC03><fC03 i1="04" i2="3" l="ENG"><s0>Unsupervised learning</s0>
<s5>07</s5>
</fC03><fC03 i1="05" i2="X" l="FRE"><s0>Fouille donnée</s0>
<s5>08</s5>
</fC03><fC03 i1="05" i2="X" l="ENG"><s0>Data mining</s0>
<s5>08</s5>
</fC03><fC03 i1="05" i2="X" l="SPA"><s0>Busca dato</s0>
<s5>08</s5>
</fC03><fC03 i1="06" i2="X" l="FRE"><s0>Système expert</s0>
<s5>09</s5>
</fC03><fC03 i1="06" i2="X" l="ENG"><s0>Expert system</s0>
<s5>09</s5>
</fC03><fC03 i1="06" i2="X" l="SPA"><s0>Sistema experto</s0>
<s5>09</s5>
</fC03><fC03 i1="07" i2="X" l="FRE"><s0>Contrôle qualité</s0>
<s5>10</s5>
</fC03><fC03 i1="07" i2="X" l="ENG"><s0>Quality control</s0>
<s5>10</s5>
</fC03><fC03 i1="07" i2="X" l="SPA"><s0>Control calidad</s0>
<s5>10</s5>
</fC03><fC03 i1="08" i2="X" l="FRE"><s0>Classification</s0>
<s5>11</s5>
</fC03><fC03 i1="08" i2="X" l="ENG"><s0>Classification</s0>
<s5>11</s5>
</fC03><fC03 i1="08" i2="X" l="SPA"><s0>Clasificación</s0>
<s5>11</s5>
</fC03><fC03 i1="09" i2="X" l="FRE"><s0>Base donnée</s0>
<s5>12</s5>
</fC03><fC03 i1="09" i2="X" l="ENG"><s0>Database</s0>
<s5>12</s5>
</fC03><fC03 i1="09" i2="X" l="SPA"><s0>Base dato</s0>
<s5>12</s5>
</fC03><fC03 i1="10" i2="X" l="FRE"><s0>Association statistique</s0>
<s5>18</s5>
</fC03><fC03 i1="10" i2="X" l="ENG"><s0>Statistical association</s0>
<s5>18</s5>
</fC03><fC03 i1="10" i2="X" l="SPA"><s0>Asociación estadística</s0>
<s5>18</s5>
</fC03><fC03 i1="11" i2="X" l="FRE"><s0>Brevet</s0>
<s5>19</s5>
</fC03><fC03 i1="11" i2="X" l="ENG"><s0>Patents</s0>
<s5>19</s5>
</fC03><fC03 i1="11" i2="X" l="SPA"><s0>Patente</s0>
<s5>19</s5>
</fC03><fC03 i1="12" i2="X" l="FRE"><s0>Propriété industrielle</s0>
<s5>20</s5>
</fC03><fC03 i1="12" i2="X" l="ENG"><s0>Patent rights</s0>
<s5>20</s5>
</fC03><fC03 i1="12" i2="X" l="SPA"><s0>Propiedad industrial</s0>
<s5>20</s5>
</fC03><fC03 i1="13" i2="X" l="FRE"><s0>Réseau neuronal</s0>
<s5>23</s5>
</fC03><fC03 i1="13" i2="X" l="ENG"><s0>Neural network</s0>
<s5>23</s5>
</fC03><fC03 i1="13" i2="X" l="SPA"><s0>Red neuronal</s0>
<s5>23</s5>
</fC03><fC03 i1="14" i2="X" l="FRE"><s0>Modélisation</s0>
<s5>24</s5>
</fC03><fC03 i1="14" i2="X" l="ENG"><s0>Modeling</s0>
<s5>24</s5>
</fC03><fC03 i1="14" i2="X" l="SPA"><s0>Modelización</s0>
<s5>24</s5>
</fC03><fC03 i1="15" i2="X" l="FRE"><s0>Base connaissance</s0>
<s5>25</s5>
</fC03><fC03 i1="15" i2="X" l="ENG"><s0>Knowledge base</s0>
<s5>25</s5>
</fC03><fC03 i1="15" i2="X" l="SPA"><s0>Base conocimiento</s0>
<s5>25</s5>
</fC03><fC03 i1="16" i2="X" l="FRE"><s0>Treillis Galois</s0>
<s5>26</s5>
</fC03><fC03 i1="16" i2="X" l="ENG"><s0>Galois lattice</s0>
<s5>26</s5>
</fC03><fC03 i1="16" i2="X" l="SPA"><s0>Retículo Galois</s0>
<s5>26</s5>
</fC03><fC03 i1="17" i2="X" l="FRE"><s0>Règle association</s0>
<s4>CD</s4>
<s5>96</s5>
</fC03><fC03 i1="17" i2="X" l="ENG"><s0>Association rule</s0>
<s4>CD</s4>
<s5>96</s5>
</fC03><fC03 i1="17" i2="X" l="SPA"><s0>Regla asociación</s0>
<s4>CD</s4>
<s5>96</s5>
</fC03><fN21><s1>037</s1>
</fN21><fN44 i1="01"><s1>OTO</s1>
</fN44><fN82><s1>OTO</s1>
</fN82><pR><fA30 i1="01" i2="1" l="ENG"><s1>International Conference on Artificial Neural Networks</s1>
<s2>15</s2>
<s3>Warsaw POL</s3>
<s4>2005-09-11</s4>
</fA30>
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