The Resource From synapses to rules : discovering symbolic rules from neural processed data, edited by Bruno Apolloni and Franz J. Kurfess
From synapses to rules : discovering symbolic rules from neural processed data, edited by Bruno Apolloni and Franz J. Kurfess
Resource Information
The item From synapses to rules : discovering symbolic rules from neural processed data, edited by Bruno Apolloni and Franz J. Kurfess represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.This item is available to borrow from 1 library branch.
Resource Information
The item From synapses to rules : discovering symbolic rules from neural processed data, edited by Bruno Apolloni and Franz J. Kurfess represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.
This item is available to borrow from 1 library branch.
- Language
- eng
- Extent
- xxii, 388 pages
- Contents
-
- 3.
- Learning Regression Functions
- Bruno Apolloni, Sabrina Gaito, Domenico Iannizzi and Dario Malchiodi.
- 4.
- Cooperative Games in a Stochastic Environment
- Bruno Apolloni, Simone Bassis, Sabrina Gaito and Dario Malchiodi.
- 5.
- If-Then-Else and Rule Extraction from Two Sets of Rules
- Daniele Mundici.
- 6.
- I.
- Extracting Interpretable Fuzzy Knowledge from Data
- Corrado Mencar
- The Theoretical Bases of Learning.
- 1.
- The Statistical Bases on Learning
- Bruno Apolloni, Simone Bassis, Sabrina Gaito and Dario Malchiodi.
- 2.
- PAC Meditation on Boolean Formulas
- Bruno Apolloni, Stefano Baraghini and Giorgio Palmas.
- Isbn
- 9780306474026
- Label
- From synapses to rules : discovering symbolic rules from neural processed data
- Title
- From synapses to rules
- Title remainder
- discovering symbolic rules from neural processed data
- Statement of responsibility
- edited by Bruno Apolloni and Franz J. Kurfess
- Language
- eng
- Cataloging source
- DLC
- Dewey number
- 006.3/2
- Illustrations
- illustrations
- Index
- index present
- LC call number
- Q334
- LC item number
- .I5756 2002
- Literary form
- non fiction
- http://bibfra.me/vocab/lite/meetingDate
- 2002
- http://bibfra.me/vocab/lite/meetingName
- International School on Neural Nets "E.R. Caianiello" Fifth Course: From Synapses to Rules: Discovering Symbolic Rules From Neural Processed Data
- Nature of contents
- bibliography
- http://library.link/vocab/relatedWorkOrContributorDate
- 1946-
- http://library.link/vocab/relatedWorkOrContributorName
-
- Apolloni, Bruno
- Kurfess, Franz
- http://library.link/vocab/subjectName
-
- Artificial intelligence
- Neural networks (Computer science)
- Machine learning
- Label
- From synapses to rules : discovering symbolic rules from neural processed data, edited by Bruno Apolloni and Franz J. Kurfess
- Bibliography note
- Includes bibliographical references and index
- Carrier category
- volume
- Carrier category code
-
- nc
- Carrier MARC source
- rdacarrier
- Content category
- text
- Content type code
-
- txt
- Content type MARC source
- rdacontent
- Contents
-
- 3.
- Learning Regression Functions
- Bruno Apolloni, Sabrina Gaito, Domenico Iannizzi and Dario Malchiodi.
- 4.
- Cooperative Games in a Stochastic Environment
- Bruno Apolloni, Simone Bassis, Sabrina Gaito and Dario Malchiodi.
- 5.
- If-Then-Else and Rule Extraction from Two Sets of Rules
- Daniele Mundici.
- 6.
- I.
- Extracting Interpretable Fuzzy Knowledge from Data
- Corrado Mencar
- The Theoretical Bases of Learning.
- 1.
- The Statistical Bases on Learning
- Bruno Apolloni, Simone Bassis, Sabrina Gaito and Dario Malchiodi.
- 2.
- PAC Meditation on Boolean Formulas
- Bruno Apolloni, Stefano Baraghini and Giorgio Palmas.
- Control code
- 50774192
- Dimensions
- 26 cm
- Extent
- xxii, 388 pages
- Isbn
- 9780306474026
- Lccn
- 2002034144
- Media category
- unmediated
- Media MARC source
- rdamedia
- Media type code
-
- n
- Other physical details
- illustrations
- Label
- From synapses to rules : discovering symbolic rules from neural processed data, edited by Bruno Apolloni and Franz J. Kurfess
- Bibliography note
- Includes bibliographical references and index
- Carrier category
- volume
- Carrier category code
-
- nc
- Carrier MARC source
- rdacarrier
- Content category
- text
- Content type code
-
- txt
- Content type MARC source
- rdacontent
- Contents
-
- 3.
- Learning Regression Functions
- Bruno Apolloni, Sabrina Gaito, Domenico Iannizzi and Dario Malchiodi.
- 4.
- Cooperative Games in a Stochastic Environment
- Bruno Apolloni, Simone Bassis, Sabrina Gaito and Dario Malchiodi.
- 5.
- If-Then-Else and Rule Extraction from Two Sets of Rules
- Daniele Mundici.
- 6.
- I.
- Extracting Interpretable Fuzzy Knowledge from Data
- Corrado Mencar
- The Theoretical Bases of Learning.
- 1.
- The Statistical Bases on Learning
- Bruno Apolloni, Simone Bassis, Sabrina Gaito and Dario Malchiodi.
- 2.
- PAC Meditation on Boolean Formulas
- Bruno Apolloni, Stefano Baraghini and Giorgio Palmas.
- Control code
- 50774192
- Dimensions
- 26 cm
- Extent
- xxii, 388 pages
- Isbn
- 9780306474026
- Lccn
- 2002034144
- Media category
- unmediated
- Media MARC source
- rdamedia
- Media type code
-
- n
- Other physical details
- illustrations
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.library.missouri.edu/portal/From-synapses-to-rules--discovering-symbolic/I-0cY5fgtP4/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.missouri.edu/portal/From-synapses-to-rules--discovering-symbolic/I-0cY5fgtP4/">From synapses to rules : discovering symbolic rules from neural processed data, edited by Bruno Apolloni and Franz J. Kurfess</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.missouri.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.missouri.edu/">University of Missouri Libraries</a></span></span></span></span></div>
