The Handbook on Reasoning-Based Intelligent Systems.:
This book consists of various contributions in conjunction with the keywords "reasoning" and "intelligent systems", which widely covers theoretical to practical aspects of intelligent systems. Therefore, it is suitable for researchers or graduate students who want to study intell...
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Format: | Elektronisch E-Book |
Sprache: | English |
Veröffentlicht: |
Singapore :
World Scientific Pub. Co.,
2013.
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Schlagworte: | |
Online-Zugang: | Volltext |
Zusammenfassung: | This book consists of various contributions in conjunction with the keywords "reasoning" and "intelligent systems", which widely covers theoretical to practical aspects of intelligent systems. Therefore, it is suitable for researchers or graduate students who want to study intelligent systems generally. |
Beschreibung: | 4.3.8 Possible Rule Generation. |
Beschreibung: | 1 online resource (680 pages) |
Bibliographie: | Includes bibliographical references. |
ISBN: | 9789814329484 9814329487 9814329479 9789814329477 9781299281288 1299281281 |
Internformat
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245 | 1 | 4 | |a The Handbook on Reasoning-Based Intelligent Systems. |
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505 | 0 | |a 6.4.2.1 Semantic specification; Preface; 1. Advances in Intelligent Systems Lakhmi C. Jain and Kazumi Nakamatsu; 1.1 Introduction; 1.2 Chapters Included in the Book; 1.3 Conclusion; 1.4 References; 1.5 Resources; 2. Stability, Chaos and Limit Cycles in Recurrent Cognitive Reasoning Systems Aruna Chakraborty, Amit Konar, Pavel Bhowmik and Atulya K. Nagar; 2.1 Introduction; 2.2 Stable Points in Propositional Temporal Dynamics; 2.2.1 Stability of propositional temporal system using Lyapunov energy function; 2.2.1.1 The Lyapunov energy function. | |
505 | 8 | |a 2.2.1.2 Asymptotic stability analysis of the propositional temporal system2.3 Stability Analysis of Fuzzy Temporal Dynamics; 2.4 Reasoning with Fuzzy Cognitive Map; 2.5 Chaos and Limit Cycles in Emotion Based Cognitive Reasoning System; 2.5.1 Effect of parameter variation on the response of the cognitive dynamics of emotion; 2.5.2 Stability analysis of the proposed emotional dynamics by Lyapunov energy function; 2.5.3 A stabilization scheme for the mixed emotional dynamics; 2.6 Conclusions; References; 3. Some Studies on Data Mining Dilip Kumar Pratihar; 3.1 Introduction. | |
505 | 8 | |a 3.2 Classification Tools3.3 Statistical Regression Analysis; 3.3.1 Design of experiments; 3.3.1.1 Full-factorial design of experiments; 3.3.1.2 Central composite design of experiments; 3.3.2 Regression analysis; 3.3.2.1 Linear regression analysis; 3.3.2.2 Non-linear regression analysis; 3.3.3 Adequacy of the model; 3.3.4 Drawbacks; 3.4 Dimensionality Reduction Techniques; 3.4.1 Sammon's Non-linear Mapping (Sammon, 1969); 3.4.2 VISOR Algorithm (Konig, 1994); 3.4.3 Self-organizing map (Kohenen, 1995); 3.4.4 GA-like approach (Dutta and Pratihar, 2006); 3.4.5 Comparisons. | |
505 | 8 | |a 3.4.6 Dimensionality reduction approaches for large data sets3.5 Clustering Techniques; 3.5.1 Fuzzy C-means algorithm (Bezdek, 1973); 3.5.2 Entropy-based fuzzy clustering (Yao et al., 2000); 3.5.3 Comparisons; 3.5.4 Clustering of large spatial data sets; 3.6 Cluster-wise Regression Analysis; 3.7 Intelligent Data Mining; 3.8 Summary; Acknowledgement; References; 4. Rough Non-deterministic Information Analysis for Uncertain Information Hiroshi Sakai, Hitomi Okuma, Mao Wu and Michinori Nakata; 4.1 Introduction; 4.2 An Overview of RNIA; 4.2.1 Basic Definitions; 4.2.2 Two Modalities in RNIA. | |
505 | 8 | |a 4.2.3 Properties and Obtained Results in RNIA4.3 Issue 1: Rule Generation on the Basis of the Consistency in NISs (Certain and Possible Rule Generation); 4.3.1 Certain Rule Generation by the Order of Attributes; 4.3.2 Minimal Certain Rules; 4.3.3 Discernibility Functions and Minimal Certain Rule Generation; 4.3.4 Enumeration Method for Obtaining Minimal Solutions; 4.3.5 Interactive Selection Method for Obtaining Minimal Solutions; 4.3.6 Interactive Selection and Enumeration Method with a Threshold Value for Obtaining Minimal Solutions; 4.3.7 Programs for ISETV-method. | |
500 | |a 4.3.8 Possible Rule Generation. | ||
520 | |a This book consists of various contributions in conjunction with the keywords "reasoning" and "intelligent systems", which widely covers theoretical to practical aspects of intelligent systems. Therefore, it is suitable for researchers or graduate students who want to study intelligent systems generally. | ||
588 | 0 | |a Print version record. | |
504 | |a Includes bibliographical references. | ||
650 | 0 | |a Artificial intelligence. |0 http://id.loc.gov/authorities/subjects/sh85008180 | |
650 | 0 | |a Reasoning. |0 http://id.loc.gov/authorities/subjects/sh85111790 | |
650 | 2 | |a Artificial Intelligence |0 https://id.nlm.nih.gov/mesh/D001185 | |
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Datensatz im Suchindex
DE-BY-FWS_katkey | ZDB-4-EBA-ocn830162006 |
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adam_text | |
any_adam_object | |
author | Nakamatsu, Kazumi |
author2 | Jain, L. C. |
author2_role | |
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author_GND | http://id.loc.gov/authorities/names/no2006063765 http://id.loc.gov/authorities/names/n95096324 |
author_facet | Nakamatsu, Kazumi Jain, L. C. |
author_role | |
author_sort | Nakamatsu, Kazumi |
author_variant | k n kn |
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callnumber-first | Q - Science |
callnumber-label | QA76 |
callnumber-raw | QA76.9.D26 |
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collection | ZDB-4-EBA |
contents | 6.4.2.1 Semantic specification; Preface; 1. Advances in Intelligent Systems Lakhmi C. Jain and Kazumi Nakamatsu; 1.1 Introduction; 1.2 Chapters Included in the Book; 1.3 Conclusion; 1.4 References; 1.5 Resources; 2. Stability, Chaos and Limit Cycles in Recurrent Cognitive Reasoning Systems Aruna Chakraborty, Amit Konar, Pavel Bhowmik and Atulya K. Nagar; 2.1 Introduction; 2.2 Stable Points in Propositional Temporal Dynamics; 2.2.1 Stability of propositional temporal system using Lyapunov energy function; 2.2.1.1 The Lyapunov energy function. 2.2.1.2 Asymptotic stability analysis of the propositional temporal system2.3 Stability Analysis of Fuzzy Temporal Dynamics; 2.4 Reasoning with Fuzzy Cognitive Map; 2.5 Chaos and Limit Cycles in Emotion Based Cognitive Reasoning System; 2.5.1 Effect of parameter variation on the response of the cognitive dynamics of emotion; 2.5.2 Stability analysis of the proposed emotional dynamics by Lyapunov energy function; 2.5.3 A stabilization scheme for the mixed emotional dynamics; 2.6 Conclusions; References; 3. Some Studies on Data Mining Dilip Kumar Pratihar; 3.1 Introduction. 3.2 Classification Tools3.3 Statistical Regression Analysis; 3.3.1 Design of experiments; 3.3.1.1 Full-factorial design of experiments; 3.3.1.2 Central composite design of experiments; 3.3.2 Regression analysis; 3.3.2.1 Linear regression analysis; 3.3.2.2 Non-linear regression analysis; 3.3.3 Adequacy of the model; 3.3.4 Drawbacks; 3.4 Dimensionality Reduction Techniques; 3.4.1 Sammon's Non-linear Mapping (Sammon, 1969); 3.4.2 VISOR Algorithm (Konig, 1994); 3.4.3 Self-organizing map (Kohenen, 1995); 3.4.4 GA-like approach (Dutta and Pratihar, 2006); 3.4.5 Comparisons. 3.4.6 Dimensionality reduction approaches for large data sets3.5 Clustering Techniques; 3.5.1 Fuzzy C-means algorithm (Bezdek, 1973); 3.5.2 Entropy-based fuzzy clustering (Yao et al., 2000); 3.5.3 Comparisons; 3.5.4 Clustering of large spatial data sets; 3.6 Cluster-wise Regression Analysis; 3.7 Intelligent Data Mining; 3.8 Summary; Acknowledgement; References; 4. Rough Non-deterministic Information Analysis for Uncertain Information Hiroshi Sakai, Hitomi Okuma, Mao Wu and Michinori Nakata; 4.1 Introduction; 4.2 An Overview of RNIA; 4.2.1 Basic Definitions; 4.2.2 Two Modalities in RNIA. 4.2.3 Properties and Obtained Results in RNIA4.3 Issue 1: Rule Generation on the Basis of the Consistency in NISs (Certain and Possible Rule Generation); 4.3.1 Certain Rule Generation by the Order of Attributes; 4.3.2 Minimal Certain Rules; 4.3.3 Discernibility Functions and Minimal Certain Rule Generation; 4.3.4 Enumeration Method for Obtaining Minimal Solutions; 4.3.5 Interactive Selection Method for Obtaining Minimal Solutions; 4.3.6 Interactive Selection and Enumeration Method with a Threshold Value for Obtaining Minimal Solutions; 4.3.7 Programs for ISETV-method. |
ctrlnum | (OCoLC)830162006 |
dewey-full | 006.3 |
dewey-hundreds | 000 - Computer science, information, general works |
dewey-ones | 006 - Special computer methods |
dewey-raw | 006.3 |
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discipline | Informatik |
format | Electronic eBook |
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Co.,</subfield><subfield code="c">2013.</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (680 pages)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">6.4.2.1 Semantic specification; Preface; 1. Advances in Intelligent Systems Lakhmi C. Jain and Kazumi Nakamatsu; 1.1 Introduction; 1.2 Chapters Included in the Book; 1.3 Conclusion; 1.4 References; 1.5 Resources; 2. Stability, Chaos and Limit Cycles in Recurrent Cognitive Reasoning Systems Aruna Chakraborty, Amit Konar, Pavel Bhowmik and Atulya K. Nagar; 2.1 Introduction; 2.2 Stable Points in Propositional Temporal Dynamics; 2.2.1 Stability of propositional temporal system using Lyapunov energy function; 2.2.1.1 The Lyapunov energy function.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">2.2.1.2 Asymptotic stability analysis of the propositional temporal system2.3 Stability Analysis of Fuzzy Temporal Dynamics; 2.4 Reasoning with Fuzzy Cognitive Map; 2.5 Chaos and Limit Cycles in Emotion Based Cognitive Reasoning System; 2.5.1 Effect of parameter variation on the response of the cognitive dynamics of emotion; 2.5.2 Stability analysis of the proposed emotional dynamics by Lyapunov energy function; 2.5.3 A stabilization scheme for the mixed emotional dynamics; 2.6 Conclusions; References; 3. 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id | ZDB-4-EBA-ocn830162006 |
illustrated | Not Illustrated |
indexdate | 2024-11-27T13:25:14Z |
institution | BVB |
isbn | 9789814329484 9814329487 9814329479 9789814329477 9781299281288 1299281281 |
language | English |
lccn | 2013427123 |
oclc_num | 830162006 |
open_access_boolean | |
owner | MAIN DE-863 DE-BY-FWS |
owner_facet | MAIN DE-863 DE-BY-FWS |
physical | 1 online resource (680 pages) |
psigel | ZDB-4-EBA |
publishDate | 2013 |
publishDateSearch | 2013 |
publishDateSort | 2013 |
publisher | World Scientific Pub. Co., |
record_format | marc |
spelling | Nakamatsu, Kazumi. http://id.loc.gov/authorities/names/no2006063765 The Handbook on Reasoning-Based Intelligent Systems. Singapore : World Scientific Pub. Co., 2013. 1 online resource (680 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier 6.4.2.1 Semantic specification; Preface; 1. Advances in Intelligent Systems Lakhmi C. Jain and Kazumi Nakamatsu; 1.1 Introduction; 1.2 Chapters Included in the Book; 1.3 Conclusion; 1.4 References; 1.5 Resources; 2. Stability, Chaos and Limit Cycles in Recurrent Cognitive Reasoning Systems Aruna Chakraborty, Amit Konar, Pavel Bhowmik and Atulya K. Nagar; 2.1 Introduction; 2.2 Stable Points in Propositional Temporal Dynamics; 2.2.1 Stability of propositional temporal system using Lyapunov energy function; 2.2.1.1 The Lyapunov energy function. 2.2.1.2 Asymptotic stability analysis of the propositional temporal system2.3 Stability Analysis of Fuzzy Temporal Dynamics; 2.4 Reasoning with Fuzzy Cognitive Map; 2.5 Chaos and Limit Cycles in Emotion Based Cognitive Reasoning System; 2.5.1 Effect of parameter variation on the response of the cognitive dynamics of emotion; 2.5.2 Stability analysis of the proposed emotional dynamics by Lyapunov energy function; 2.5.3 A stabilization scheme for the mixed emotional dynamics; 2.6 Conclusions; References; 3. Some Studies on Data Mining Dilip Kumar Pratihar; 3.1 Introduction. 3.2 Classification Tools3.3 Statistical Regression Analysis; 3.3.1 Design of experiments; 3.3.1.1 Full-factorial design of experiments; 3.3.1.2 Central composite design of experiments; 3.3.2 Regression analysis; 3.3.2.1 Linear regression analysis; 3.3.2.2 Non-linear regression analysis; 3.3.3 Adequacy of the model; 3.3.4 Drawbacks; 3.4 Dimensionality Reduction Techniques; 3.4.1 Sammon's Non-linear Mapping (Sammon, 1969); 3.4.2 VISOR Algorithm (Konig, 1994); 3.4.3 Self-organizing map (Kohenen, 1995); 3.4.4 GA-like approach (Dutta and Pratihar, 2006); 3.4.5 Comparisons. 3.4.6 Dimensionality reduction approaches for large data sets3.5 Clustering Techniques; 3.5.1 Fuzzy C-means algorithm (Bezdek, 1973); 3.5.2 Entropy-based fuzzy clustering (Yao et al., 2000); 3.5.3 Comparisons; 3.5.4 Clustering of large spatial data sets; 3.6 Cluster-wise Regression Analysis; 3.7 Intelligent Data Mining; 3.8 Summary; Acknowledgement; References; 4. Rough Non-deterministic Information Analysis for Uncertain Information Hiroshi Sakai, Hitomi Okuma, Mao Wu and Michinori Nakata; 4.1 Introduction; 4.2 An Overview of RNIA; 4.2.1 Basic Definitions; 4.2.2 Two Modalities in RNIA. 4.2.3 Properties and Obtained Results in RNIA4.3 Issue 1: Rule Generation on the Basis of the Consistency in NISs (Certain and Possible Rule Generation); 4.3.1 Certain Rule Generation by the Order of Attributes; 4.3.2 Minimal Certain Rules; 4.3.3 Discernibility Functions and Minimal Certain Rule Generation; 4.3.4 Enumeration Method for Obtaining Minimal Solutions; 4.3.5 Interactive Selection Method for Obtaining Minimal Solutions; 4.3.6 Interactive Selection and Enumeration Method with a Threshold Value for Obtaining Minimal Solutions; 4.3.7 Programs for ISETV-method. 4.3.8 Possible Rule Generation. This book consists of various contributions in conjunction with the keywords "reasoning" and "intelligent systems", which widely covers theoretical to practical aspects of intelligent systems. Therefore, it is suitable for researchers or graduate students who want to study intelligent systems generally. Print version record. Includes bibliographical references. Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Reasoning. http://id.loc.gov/authorities/subjects/sh85111790 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Intelligence artificielle. artificial intelligence. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Artificial intelligence fast Reasoning fast Jain, L. C. http://id.loc.gov/authorities/names/n95096324 has work: The handbook on reasoning-based intelligent systems (Text) https://id.oclc.org/worldcat/entity/E39PCGF3mDgvB8CR4xq6d9CgjC https://id.oclc.org/worldcat/ontology/hasWork Print version: Nakamatsu, Kazumi. Handbook on Reasoning-Based Intelligent Systems. Singapore : World Scientific Publishing Company, ©2013 9789814329477 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=545484 Volltext |
spellingShingle | Nakamatsu, Kazumi The Handbook on Reasoning-Based Intelligent Systems. 6.4.2.1 Semantic specification; Preface; 1. Advances in Intelligent Systems Lakhmi C. Jain and Kazumi Nakamatsu; 1.1 Introduction; 1.2 Chapters Included in the Book; 1.3 Conclusion; 1.4 References; 1.5 Resources; 2. Stability, Chaos and Limit Cycles in Recurrent Cognitive Reasoning Systems Aruna Chakraborty, Amit Konar, Pavel Bhowmik and Atulya K. Nagar; 2.1 Introduction; 2.2 Stable Points in Propositional Temporal Dynamics; 2.2.1 Stability of propositional temporal system using Lyapunov energy function; 2.2.1.1 The Lyapunov energy function. 2.2.1.2 Asymptotic stability analysis of the propositional temporal system2.3 Stability Analysis of Fuzzy Temporal Dynamics; 2.4 Reasoning with Fuzzy Cognitive Map; 2.5 Chaos and Limit Cycles in Emotion Based Cognitive Reasoning System; 2.5.1 Effect of parameter variation on the response of the cognitive dynamics of emotion; 2.5.2 Stability analysis of the proposed emotional dynamics by Lyapunov energy function; 2.5.3 A stabilization scheme for the mixed emotional dynamics; 2.6 Conclusions; References; 3. Some Studies on Data Mining Dilip Kumar Pratihar; 3.1 Introduction. 3.2 Classification Tools3.3 Statistical Regression Analysis; 3.3.1 Design of experiments; 3.3.1.1 Full-factorial design of experiments; 3.3.1.2 Central composite design of experiments; 3.3.2 Regression analysis; 3.3.2.1 Linear regression analysis; 3.3.2.2 Non-linear regression analysis; 3.3.3 Adequacy of the model; 3.3.4 Drawbacks; 3.4 Dimensionality Reduction Techniques; 3.4.1 Sammon's Non-linear Mapping (Sammon, 1969); 3.4.2 VISOR Algorithm (Konig, 1994); 3.4.3 Self-organizing map (Kohenen, 1995); 3.4.4 GA-like approach (Dutta and Pratihar, 2006); 3.4.5 Comparisons. 3.4.6 Dimensionality reduction approaches for large data sets3.5 Clustering Techniques; 3.5.1 Fuzzy C-means algorithm (Bezdek, 1973); 3.5.2 Entropy-based fuzzy clustering (Yao et al., 2000); 3.5.3 Comparisons; 3.5.4 Clustering of large spatial data sets; 3.6 Cluster-wise Regression Analysis; 3.7 Intelligent Data Mining; 3.8 Summary; Acknowledgement; References; 4. Rough Non-deterministic Information Analysis for Uncertain Information Hiroshi Sakai, Hitomi Okuma, Mao Wu and Michinori Nakata; 4.1 Introduction; 4.2 An Overview of RNIA; 4.2.1 Basic Definitions; 4.2.2 Two Modalities in RNIA. 4.2.3 Properties and Obtained Results in RNIA4.3 Issue 1: Rule Generation on the Basis of the Consistency in NISs (Certain and Possible Rule Generation); 4.3.1 Certain Rule Generation by the Order of Attributes; 4.3.2 Minimal Certain Rules; 4.3.3 Discernibility Functions and Minimal Certain Rule Generation; 4.3.4 Enumeration Method for Obtaining Minimal Solutions; 4.3.5 Interactive Selection Method for Obtaining Minimal Solutions; 4.3.6 Interactive Selection and Enumeration Method with a Threshold Value for Obtaining Minimal Solutions; 4.3.7 Programs for ISETV-method. Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Reasoning. http://id.loc.gov/authorities/subjects/sh85111790 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Intelligence artificielle. artificial intelligence. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Artificial intelligence fast Reasoning fast |
subject_GND | http://id.loc.gov/authorities/subjects/sh85008180 http://id.loc.gov/authorities/subjects/sh85111790 https://id.nlm.nih.gov/mesh/D001185 |
title | The Handbook on Reasoning-Based Intelligent Systems. |
title_auth | The Handbook on Reasoning-Based Intelligent Systems. |
title_exact_search | The Handbook on Reasoning-Based Intelligent Systems. |
title_full | The Handbook on Reasoning-Based Intelligent Systems. |
title_fullStr | The Handbook on Reasoning-Based Intelligent Systems. |
title_full_unstemmed | The Handbook on Reasoning-Based Intelligent Systems. |
title_short | The Handbook on Reasoning-Based Intelligent Systems. |
title_sort | handbook on reasoning based intelligent systems |
topic | Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Reasoning. http://id.loc.gov/authorities/subjects/sh85111790 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Intelligence artificielle. artificial intelligence. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Artificial intelligence fast Reasoning fast |
topic_facet | Artificial intelligence. Reasoning. Artificial Intelligence Intelligence artificielle. artificial intelligence. COMPUTERS Enterprise Applications Business Intelligence Tools. COMPUTERS Intelligence (AI) & Semantics. Artificial intelligence Reasoning |
url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=545484 |
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