Soft Computing for Knowledge Discovery: Introducing Cartesian Granule Features
Knowledge discovery is an area of computer science that attempts to uncover interesting and useful patterns in data that permit a computer to perform a task autonomously or assist a human in performing a task more efficiently. Soft Computing for Knowledge Discovery provides a self-contained and syst...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Boston, MA
Springer US
2000
|
| Schriftenreihe: | The Springer International Series in Engineering and Computer Science
570 |
| Schlagwörter: | |
| Links: | https://doi.org/10.1007/978-1-4615-4335-0 https://doi.org/10.1007/978-1-4615-4335-0 https://doi.org/10.1007/978-1-4615-4335-0 |
| Zusammenfassung: | Knowledge discovery is an area of computer science that attempts to uncover interesting and useful patterns in data that permit a computer to perform a task autonomously or assist a human in performing a task more efficiently. Soft Computing for Knowledge Discovery provides a self-contained and systematic exposition of the key theory and algorithms that form the core of knowledge discovery from a soft computing perspective. It focuses on knowledge representation, machine learning, and the key methodologies that make up the fabric of soft computing - fuzzy set theory, fuzzy logic, evolutionary computing, and various theories of probability (e.g. naïve Bayes and Bayesian networks, Dempster-Shafer theory, mass assignment theory, and others). In addition to describing many state-of-the-art soft computing approaches to knowledge discovery, the author introduces Cartesian granule features and their corresponding learning algorithms as an intuitive approach to knowledge discovery. This new approach embraces the synergistic spirit of soft computing and exploits uncertainty in order to achieve tractability, transparency and generalization. Parallels are drawn between this approach and other well known approaches (such as naive Bayes and decision trees) leading to equivalences under certain conditions. The approaches presented are further illustrated in a battery of both artificial and real-world problems. Knowledge discovery in real-world problems, such as object recognition in outdoor scenes, medical diagnosis and control, is described in detail. These case studies provide further examples of how to apply the presented concepts and algorithms to practical problems. The author provides web page access to an online bibliography, datasets, source codes for several algorithms described in the book, and other information. Soft Computing for Knowledge Discovery is for advanced undergraduates, professionals and researchers in computer science, engineering and business information systems who work or have an interest in the dynamic fields of knowledge discovery and soft computing |
| Umfang: | 1 Online-Ressource (XXI, 326 p) |
| ISBN: | 9781461543350 |
| DOI: | 10.1007/978-1-4615-4335-0 |
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| 520 | |a Knowledge discovery is an area of computer science that attempts to uncover interesting and useful patterns in data that permit a computer to perform a task autonomously or assist a human in performing a task more efficiently. Soft Computing for Knowledge Discovery provides a self-contained and systematic exposition of the key theory and algorithms that form the core of knowledge discovery from a soft computing perspective. It focuses on knowledge representation, machine learning, and the key methodologies that make up the fabric of soft computing - fuzzy set theory, fuzzy logic, evolutionary computing, and various theories of probability (e.g. naïve Bayes and Bayesian networks, Dempster-Shafer theory, mass assignment theory, and others). In addition to describing many state-of-the-art soft computing approaches to knowledge discovery, the author introduces Cartesian granule features and their corresponding learning algorithms as an intuitive approach to knowledge discovery. | ||
| 520 | |a This new approach embraces the synergistic spirit of soft computing and exploits uncertainty in order to achieve tractability, transparency and generalization. Parallels are drawn between this approach and other well known approaches (such as naive Bayes and decision trees) leading to equivalences under certain conditions. The approaches presented are further illustrated in a battery of both artificial and real-world problems. Knowledge discovery in real-world problems, such as object recognition in outdoor scenes, medical diagnosis and control, is described in detail. These case studies provide further examples of how to apply the presented concepts and algorithms to practical problems. The author provides web page access to an online bibliography, datasets, source codes for several algorithms described in the book, and other information. | ||
| 520 | |a Soft Computing for Knowledge Discovery is for advanced undergraduates, professionals and researchers in computer science, engineering and business information systems who work or have an interest in the dynamic fields of knowledge discovery and soft computing | ||
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Datensatz im Suchindex
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|---|---|
| any_adam_object | |
| author | Shanahan, James G. |
| author_facet | Shanahan, James G. |
| author_role | aut |
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| author_variant | j g s jg jgs |
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| dewey-ones | 005 - Computer programming, programs, data, security |
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| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
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| id | DE-604.BV045148955 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T18:19:19Z |
| institution | BVB |
| isbn | 9781461543350 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030538654 |
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| spelling | Shanahan, James G. Verfasser aut Soft Computing for Knowledge Discovery Introducing Cartesian Granule Features by James G. Shanahan Boston, MA Springer US 2000 1 Online-Ressource (XXI, 326 p) txt rdacontent c rdamedia cr rdacarrier The Springer International Series in Engineering and Computer Science 570 Knowledge discovery is an area of computer science that attempts to uncover interesting and useful patterns in data that permit a computer to perform a task autonomously or assist a human in performing a task more efficiently. Soft Computing for Knowledge Discovery provides a self-contained and systematic exposition of the key theory and algorithms that form the core of knowledge discovery from a soft computing perspective. It focuses on knowledge representation, machine learning, and the key methodologies that make up the fabric of soft computing - fuzzy set theory, fuzzy logic, evolutionary computing, and various theories of probability (e.g. naïve Bayes and Bayesian networks, Dempster-Shafer theory, mass assignment theory, and others). In addition to describing many state-of-the-art soft computing approaches to knowledge discovery, the author introduces Cartesian granule features and their corresponding learning algorithms as an intuitive approach to knowledge discovery. This new approach embraces the synergistic spirit of soft computing and exploits uncertainty in order to achieve tractability, transparency and generalization. Parallels are drawn between this approach and other well known approaches (such as naive Bayes and decision trees) leading to equivalences under certain conditions. The approaches presented are further illustrated in a battery of both artificial and real-world problems. Knowledge discovery in real-world problems, such as object recognition in outdoor scenes, medical diagnosis and control, is described in detail. These case studies provide further examples of how to apply the presented concepts and algorithms to practical problems. The author provides web page access to an online bibliography, datasets, source codes for several algorithms described in the book, and other information. Soft Computing for Knowledge Discovery is for advanced undergraduates, professionals and researchers in computer science, engineering and business information systems who work or have an interest in the dynamic fields of knowledge discovery and soft computing Computer Science Information Systems Applications (incl. Internet) Mathematical Logic and Foundations Artificial Intelligence (incl. Robotics) Data Structures, Cryptology and Information Theory Computer Science, general Computer science Data structures (Computer science) Artificial intelligence Mathematical logic Soft Computing (DE-588)4455833-8 gnd rswk-swf Wissensextraktion (DE-588)4546354-2 gnd rswk-swf Soft Computing (DE-588)4455833-8 s 1\p DE-604 Wissensextraktion (DE-588)4546354-2 s 2\p DE-604 Erscheint auch als Druck-Ausgabe 9781461369479 https://doi.org/10.1007/978-1-4615-4335-0 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Shanahan, James G. Soft Computing for Knowledge Discovery Introducing Cartesian Granule Features Computer Science Information Systems Applications (incl. Internet) Mathematical Logic and Foundations Artificial Intelligence (incl. Robotics) Data Structures, Cryptology and Information Theory Computer Science, general Computer science Data structures (Computer science) Artificial intelligence Mathematical logic Soft Computing (DE-588)4455833-8 gnd Wissensextraktion (DE-588)4546354-2 gnd |
| subject_GND | (DE-588)4455833-8 (DE-588)4546354-2 |
| title | Soft Computing for Knowledge Discovery Introducing Cartesian Granule Features |
| title_auth | Soft Computing for Knowledge Discovery Introducing Cartesian Granule Features |
| title_exact_search | Soft Computing for Knowledge Discovery Introducing Cartesian Granule Features |
| title_full | Soft Computing for Knowledge Discovery Introducing Cartesian Granule Features by James G. Shanahan |
| title_fullStr | Soft Computing for Knowledge Discovery Introducing Cartesian Granule Features by James G. Shanahan |
| title_full_unstemmed | Soft Computing for Knowledge Discovery Introducing Cartesian Granule Features by James G. Shanahan |
| title_short | Soft Computing for Knowledge Discovery |
| title_sort | soft computing for knowledge discovery introducing cartesian granule features |
| title_sub | Introducing Cartesian Granule Features |
| topic | Computer Science Information Systems Applications (incl. Internet) Mathematical Logic and Foundations Artificial Intelligence (incl. Robotics) Data Structures, Cryptology and Information Theory Computer Science, general Computer science Data structures (Computer science) Artificial intelligence Mathematical logic Soft Computing (DE-588)4455833-8 gnd Wissensextraktion (DE-588)4546354-2 gnd |
| topic_facet | Computer Science Information Systems Applications (incl. Internet) Mathematical Logic and Foundations Artificial Intelligence (incl. Robotics) Data Structures, Cryptology and Information Theory Computer Science, general Computer science Data structures (Computer science) Artificial intelligence Mathematical logic Soft Computing Wissensextraktion |
| url | https://doi.org/10.1007/978-1-4615-4335-0 |
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