Kernels for structured data:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore
World Scientific Pub. Co.
©2008
|
| Schriftenreihe: | Series in machine perception and artificial intelligence
v. 72 |
| Schlagworte: | |
| Online-Zugang: | FWS01 FWS02 Volltext |
| Beschreibung: | 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 |
| Beschreibung: | 1 Online-Ressource (216 Seiten) |
| ISBN: | 9789814471039 9789812814562 9812814566 |
Internformat
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| 490 | 0 | |a Series in machine perception and artificial intelligence |v v. 72 | |
| 500 | |a Includes bibliographical references (pages 179-190) and index | ||
| 500 | |a 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 | ||
| 500 | |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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Datensatz im Suchindex
| DE-BY-FWS_katkey | 924678 |
|---|---|
| _version_ | 1806194910309646336 |
| any_adam_object | |
| author | Gärtner, Thomas |
| author_facet | Gärtner, Thomas |
| author_role | aut |
| author_sort | Gärtner, Thomas |
| author_variant | t g tg |
| building | Verbundindex |
| bvnumber | BV042961142 |
| classification_rvk | ST 300 ST 302 |
| collection | ZDB-4-EBU ZDB-124-WOP |
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| dewey-full | 006.31 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.31 |
| dewey-search | 006.31 |
| dewey-sort | 16.31 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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| id | DE-604.BV042961142 |
| illustrated | Not Illustrated |
| indexdate | 2024-08-01T16:14:55Z |
| institution | BVB |
| isbn | 9789814471039 9789812814562 9812814566 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028387010 |
| oclc_num | 820944529 |
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| owner | DE-863 DE-BY-FWS DE-862 DE-BY-FWS |
| owner_facet | DE-863 DE-BY-FWS DE-862 DE-BY-FWS |
| physical | 1 Online-Ressource (216 Seiten) |
| psigel | ZDB-4-EBU ZDB-124-WOP FLA_PDA_EBU |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | World Scientific Pub. Co. |
| record_format | marc |
| series2 | Series in machine perception and artificial intelligence |
| spellingShingle | Gärtner, Thomas Kernels for structured data COMPUTERS / Enterprise Applications / Business Intelligence Tools bisacsh COMPUTERS / Intelligence (AI) & Semantics bisacsh Kernel functions fast Machine learning fast Machine learning Kernel functions Strukturierte Daten (DE-588)4620514-7 gnd Klassifikator Informatik (DE-588)4288547-4 gnd |
| subject_GND | (DE-588)4620514-7 (DE-588)4288547-4 |
| 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 | COMPUTERS / Enterprise Applications / Business Intelligence Tools bisacsh COMPUTERS / Intelligence (AI) & Semantics bisacsh Kernel functions fast Machine learning fast Machine learning Kernel functions Strukturierte Daten (DE-588)4620514-7 gnd Klassifikator Informatik (DE-588)4288547-4 gnd |
| topic_facet | COMPUTERS / Enterprise Applications / Business Intelligence Tools COMPUTERS / Intelligence (AI) & Semantics Kernel functions Machine learning Strukturierte Daten Klassifikator Informatik |
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| work_keys_str_mv | AT gartnerthomas kernelsforstructureddata |