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Graph-theoretic techniques for web content mining /
This book describes exciting new opportunities for utilizing robust graph representations of data with common machine learning algorithms. Graphs can model additional information which is often not present in commonly used data representations, such as vectors.
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Singapore ; Hackensack, N.J. :
World Scientific,
©2005.
|
| Colección: | Series in machine perception and artificial intelligence ;
v. 62. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 0 | 0 | |a Graph-theoretic techniques for web content mining / |c Adam Schenker [and others]. |
| 260 | |a Singapore ; |a Hackensack, N.J. : |b World Scientific, |c ©2005. | ||
| 300 | |a 1 online resource (249 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Series in machine perception and artificial intelligence ; |v v. 62 | |
| 500 | |a Title from title screen. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a This book describes exciting new opportunities for utilizing robust graph representations of data with common machine learning algorithms. Graphs can model additional information which is often not present in commonly used data representations, such as vectors. | ||
| 505 | 0 | |a Preface; Contents; Chapter 1 Introduction to Web Mining; Chapter 2 Graph Similarity Techniques; Chapter 3 Graph Models for Web Documents; Chapter 4 Graph-Based Clustering; Chapter 5 Graph-Based Classification; Chapter 6 The Graph Hierarchy Construction Algorithm for Web Search Clustering; Chapter 7 Conclusions and Future Work; Appendix A Graph Examples; Appendix B List of Stop Words; Bibliography; Index. | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Graph theory |x Data processing. | |
| 650 | 0 | |a Algorithms. | |
| 650 | 0 | |a Multidimensional scaling. | |
| 650 | 0 | |a Computer algorithms. | |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 650 | 6 | |a Algorithmes. | |
| 650 | 6 | |a Échelle multidimensionnelle. | |
| 650 | 7 | |a algorithms. |2 aat | |
| 650 | 7 | |a Computer algorithms |2 fast | |
| 650 | 7 | |a Algorithms |2 fast | |
| 650 | 7 | |a Data mining |2 fast | |
| 650 | 7 | |a Graph theory |x Data processing |2 fast | |
| 650 | 7 | |a Multidimensional scaling |2 fast | |
| 700 | 1 | |a Schenker, Adam. | |
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| 758 | |i has work: |a Graph-theoretic techniques for web content mining (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFPJ7jM8W4DRkBCYhGRcpq |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
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| 830 | 0 | |a Series in machine perception and artificial intelligence ; |v v. 62. | |
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