Kernels for structured data /:
This book provides a unique treatment of an important area of machine learning and answers the question of how kernel methods can be applied to structured data. Kernel methods are a class of state-of-the-art learning algorithms that exhibit excellent learning results in several application domains....
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore ; Hackensack, N.J. :
World Scientific Pub. Co.,
©2008.
|
| Schriftenreihe: | Series in machine perception and artificial intelligence ;
v. 72. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | This book provides a unique treatment of an important area of machine learning and answers the question of how kernel methods can be applied to structured data. Kernel methods are a class of state-of-the-art learning algorithms that exhibit excellent learning results in several application domains. Originally, kernel methods were developed with data in mind that can easily be embedded in a Euclidean vector space. Much real-world data does not have this property but is inherently structured. An example of such data, often consulted in the book, is the (2D) graph structure of molecules formed by their atoms and bonds. The book guides the reader from the basics of kernel methods to advanced algorithms and kernel design for structured data. It is thus useful for readers who seek an entry point into the field as well as experienced researchers. |
| Beschreibung: | 1 online resource |
| Bibliographie: | Includes bibliographical references (pages 179-190) and index. |
| ISBN: | 9789812814562 9812814566 |
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| 520 | |a This book provides a unique treatment of an important area of machine learning and answers the question of how kernel methods can be applied to structured data. Kernel methods are a class of state-of-the-art learning algorithms that exhibit excellent learning results in several application domains. Originally, kernel methods were developed with data in mind that can easily be embedded in a Euclidean vector space. Much real-world data does not have this property but is inherently structured. An example of such data, often consulted in the book, is the (2D) graph structure of molecules formed by their atoms and bonds. The book guides the reader from the basics of kernel methods to advanced algorithms and kernel design for structured data. It is thus useful for readers who seek an entry point into the field as well as experienced researchers. | ||
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| contents | 1. Why kernels for structured data? 1.1. Supervised machine learning. 1.2. Kernel methods. 1.3. Representing structured data. 1.4. Goals and contributions. 1.5. Outline. 1.6. Bibliographical notes -- 2. Kernel methods in a nutshell. 2.1. Mathematical foundations. 2.2. Recognising patterns with kernels. 2.3. Foundations of kernel methods. 2.4. Kernel machines. 2.5. Summary -- 3. Kernel design. 3.1. General remarks on kernels and examples. 3.2. Kernel functions. 3.3. Introduction to kernels for structured data. 3.4. Prior work. 3.5. Summary -- 4. Basic term kernels. 4.1. Logics for learning. 4.2. Kernels for basic terms. 4.3. Multi-instance learning. 4.4. Related work. 4.5. Applications and experiments -- 5. Graph kernels. 5.1. Motivation and approach. 5.2. Labelled directed graphs. 5.3. Complete graph kernels. 5.4. Walk kernels. 5.5. Cyclic pattern kernels. 5.6. Related work. 5.7. Relational reinforcement learning. 5.8. Molecule classification. 5.9 Summary -- 6. Conclusions. |
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| indexdate | 2024-11-26T14:49:07Z |
| institution | BVB |
| isbn | 9789812814562 9812814566 |
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| series2 | Series in machine perception and artificial intelligence ; |
| spelling | Gärtner, Thomas. Kernels for structured data / Thomas Gärtner. Singapore ; Hackensack, N.J. : World Scientific Pub. Co., ©2008. 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier Series in machine perception and artificial intelligence ; v. 72 Includes bibliographical references (pages 179-190) and index. 1. Why kernels for structured data? 1.1. Supervised machine learning. 1.2. Kernel methods. 1.3. Representing structured data. 1.4. Goals and contributions. 1.5. Outline. 1.6. Bibliographical notes -- 2. Kernel methods in a nutshell. 2.1. Mathematical foundations. 2.2. Recognising patterns with kernels. 2.3. Foundations of kernel methods. 2.4. Kernel machines. 2.5. Summary -- 3. Kernel design. 3.1. General remarks on kernels and examples. 3.2. Kernel functions. 3.3. Introduction to kernels for structured data. 3.4. Prior work. 3.5. Summary -- 4. Basic term kernels. 4.1. Logics for learning. 4.2. Kernels for basic terms. 4.3. Multi-instance learning. 4.4. Related work. 4.5. Applications and experiments -- 5. Graph kernels. 5.1. Motivation and approach. 5.2. Labelled directed graphs. 5.3. Complete graph kernels. 5.4. Walk kernels. 5.5. Cyclic pattern kernels. 5.6. Related work. 5.7. Relational reinforcement learning. 5.8. Molecule classification. 5.9 Summary -- 6. Conclusions. This book provides a unique treatment of an important area of machine learning and answers the question of how kernel methods can be applied to structured data. Kernel methods are a class of state-of-the-art learning algorithms that exhibit excellent learning results in several application domains. Originally, kernel methods were developed with data in mind that can easily be embedded in a Euclidean vector space. Much real-world data does not have this property but is inherently structured. An example of such data, often consulted in the book, is the (2D) graph structure of molecules formed by their atoms and bonds. The book guides the reader from the basics of kernel methods to advanced algorithms and kernel design for structured data. It is thus useful for readers who seek an entry point into the field as well as experienced researchers. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Kernel functions. http://id.loc.gov/authorities/subjects/sh85072061 Apprentissage automatique. Noyaux (Mathématiques) COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Kernel functions fast Machine learning fast Maschinelles Lernen gnd http://d-nb.info/gnd/4193754-5 Original 9812814558 9789812814555 (DLC) 2009277142 (OCoLC)228425525 Series in machine perception and artificial intelligence ; v. 72. http://id.loc.gov/authorities/names/n91107585 FWS01 ZDB-4-EBU FWS_PDA_EBU https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=521165 Volltext |
| spellingShingle | Gärtner, Thomas Kernels for structured data / Series in machine perception and artificial intelligence ; 1. Why kernels for structured data? 1.1. Supervised machine learning. 1.2. Kernel methods. 1.3. Representing structured data. 1.4. Goals and contributions. 1.5. Outline. 1.6. Bibliographical notes -- 2. Kernel methods in a nutshell. 2.1. Mathematical foundations. 2.2. Recognising patterns with kernels. 2.3. Foundations of kernel methods. 2.4. Kernel machines. 2.5. Summary -- 3. Kernel design. 3.1. General remarks on kernels and examples. 3.2. Kernel functions. 3.3. Introduction to kernels for structured data. 3.4. Prior work. 3.5. Summary -- 4. Basic term kernels. 4.1. Logics for learning. 4.2. Kernels for basic terms. 4.3. Multi-instance learning. 4.4. Related work. 4.5. Applications and experiments -- 5. Graph kernels. 5.1. Motivation and approach. 5.2. Labelled directed graphs. 5.3. Complete graph kernels. 5.4. Walk kernels. 5.5. Cyclic pattern kernels. 5.6. Related work. 5.7. Relational reinforcement learning. 5.8. Molecule classification. 5.9 Summary -- 6. Conclusions. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Kernel functions. http://id.loc.gov/authorities/subjects/sh85072061 Apprentissage automatique. Noyaux (Mathématiques) COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Kernel functions fast Machine learning fast Maschinelles Lernen gnd http://d-nb.info/gnd/4193754-5 |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh85072061 http://d-nb.info/gnd/4193754-5 |
| title | Kernels for structured data / |
| title_auth | Kernels for structured data / |
| title_exact_search | Kernels for structured data / |
| title_full | Kernels for structured data / Thomas Gärtner. |
| title_fullStr | Kernels for structured data / Thomas Gärtner. |
| title_full_unstemmed | Kernels for structured data / Thomas Gärtner. |
| title_short | Kernels for structured data / |
| title_sort | kernels for structured data |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Kernel functions. http://id.loc.gov/authorities/subjects/sh85072061 Apprentissage automatique. Noyaux (Mathématiques) COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Kernel functions fast Machine learning fast Maschinelles Lernen gnd http://d-nb.info/gnd/4193754-5 |
| topic_facet | Machine learning. Kernel functions. Apprentissage automatique. Noyaux (Mathématiques) COMPUTERS Enterprise Applications Business Intelligence Tools. COMPUTERS Intelligence (AI) & Semantics. Kernel functions Machine learning Maschinelles Lernen |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=521165 |
| work_keys_str_mv | AT gartnerthomas kernelsforstructureddata |