Search (6 results, page 1 of 1)

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Content

Inhalt: Introduction Document File Preparation - Manual Indexing - Information Extraction - Vector Space Modeling - Matrix Decompositions - Query Representations - Ranking and Relevance Feedback - Searching by Link Structure - User Interface - Book Format Document File Preparation Document Purification and Analysis - Text Formatting - Validation - Manual Indexing - Automatic Indexing - Item Normalization - Inverted File Structures - Document File - Dictionary List - Inversion List - Other File Structures Vector Space Models Construction - Term-by-Document Matrices - Simple Query Matching - Design Issues - Term Weighting - Sparse Matrix Storage - Low-Rank Approximations Matrix Decompositions QR Factorization - Singular Value Decomposition - Low-Rank Approximations - Query Matching - Software - Semidiscrete Decomposition - Updating Techniques Query Management Query Binding - Types of Queries - Boolean Queries - Natural Language Queries - Thesaurus Queries - Fuzzy Queries - Term Searches - Probabilistic Queries Ranking and Relevance Feedback Performance Evaluation - Precision - Recall - Average Precision - Genetic Algorithms - Relevance Feedback Searching by Link Structure HITS Method - HITS Implementation - HITS Summary - PageRank Method - PageRank Adjustments - PageRank Implementation - PageRank Summary User Interface Considerations General Guidelines - Search Engine Interfaces - Form Fill-in - Display Considerations - Progress Indication - No Penalties for Error - Results - Test and Retest - Final Considerations Further Reading

RSWK

Suchmaschine / Information Retrieval
Suchmaschine / Information Retrieval / Mathematisches Modell (HEBIS)

Subject

Suchmaschine / Information Retrieval
Suchmaschine / Information Retrieval / Mathematisches Modell (HEBIS)

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Content

Inhalt: Chapter 1. Introduction to Web Search Engines: 1.1 A Short History of Information Retrieval - 1.2 An Overview of Traditional Information Retrieval - 1.3 Web Information Retrieval Chapter 2. Crawling, Indexing, and Query Processing: 2.1 Crawling - 2.2 The Content Index - 2.3 Query Processing Chapter 3. Ranking Webpages by Popularity: 3.1 The Scene in 1998 - 3.2 Two Theses - 3.3 Query-Independence Chapter 4. The Mathematics of Google's PageRank: 4.1 The Original Summation Formula for PageRank - 4.2 Matrix Representation of the Summation Equations - 4.3 Problems with the Iterative Process - 4.4 A Little Markov Chain Theory - 4.5 Early Adjustments to the Basic Model - 4.6 Computation of the PageRank Vector - 4.7 Theorem and Proof for Spectrum of the Google Matrix Chapter 5. Parameters in the PageRank Model: 5.1 The a Factor - 5.2 The Hyperlink Matrix H - 5.3 The Teleportation Matrix E Chapter 6. The Sensitivity of PageRank; 6.1 Sensitivity with respect to alpha - 6.2 Sensitivity with respect to H - 6.3 Sensitivity with respect to vT - 6.4 Other Analyses of Sensitivity - 6.5 Sensitivity Theorems and Proofs Chapter 7. The PageRank Problem as a Linear System: 7.1 Properties of (I - alphaS) - 7.2 Properties of (I - alphaH) - 7.3 Proof of the PageRank Sparse Linear System Chapter 8. Issues in Large-Scale Implementation of PageRank: 8.1 Storage Issues - 8.2 Convergence Criterion - 8.3 Accuracy - 8.4 Dangling Nodes - 8.5 Back Button Modeling
Chapter 9. Accelerating the Computation of PageRank: 9.1 An Adaptive Power Method - 9.2 Extrapolation - 9.3 Aggregation - 9.4 Other Numerical Methods Chapter 10. Updating the PageRank Vector: 10.1 The Two Updating Problems and their History - 10.2 Restarting the Power Method - 10.3 Approximate Updating Using Approximate Aggregation - 10.4 Exact Aggregation - 10.5 Exact vs. Approximate Aggregation - 10.6 Updating with Iterative Aggregation - 10.7 Determining the Partition - 10.8 Conclusions Chapter 11. The HITS Method for Ranking Webpages: 11.1 The HITS Algorithm - 11.2 HITS Implementation - 11.3 HITS Convergence - 11.4 HITS Example - 11.5 Strengths and Weaknesses of HITS - 11.6 HITS's Relationship to Bibliometrics - 11.7 Query-Independent HITS - 11.8 Accelerating HITS - 11.9 HITS Sensitivity Chapter 12. Other Link Methods for Ranking Webpages: 12.1 SALSA - 12.2 Hybrid Ranking Methods - 12.3 Rankings based on Traffic Flow Chapter 13. The Future of Web Information Retrieval: 13.1 Spam - 13.2 Personalization - 13.3 Clustering - 13.4 Intelligent Agents - 13.5 Trends and Time-Sensitive Search - 13.6 Privacy and Censorship - 13.7 Library Classification Schemes - 13.8 Data Fusion Chapter 14. Resources for Web Information Retrieval: 14.1 Resources for Getting Started - 14.2 Resources for Serious Study Chapter 15. The Mathematics Guide: 15.1 Linear Algebra - 15.2 Perron-Frobenius Theory - 15.3 Markov Chains - 15.4 Perron Complementation - 15.5 Stochastic Complementation - 15.6 Censoring - 15.7 Aggregation - 15.8 Disaggregation

RSWK

Google / Suchmaschine / Ranking (BVB)

Subject

Google / Suchmaschine / Ranking (BVB)

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RSWK

World Wide Web 2.0 / Suchmaschine

Subject

World Wide Web 2.0 / Suchmaschine

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Footnote

Rez. in: FR Nr.165 vom 18.7.2003, S.14 (T.P. Gangloff) "Suchmaschinen sind unverzichtbare Helferinnen für die Internet-Recherche Doch wenn die Trefferliste zu viele Links anbietet, wird die Suche schon mal zur schlafraubenden Odyssee. Wer angesichts umfangreicher Trefferlisten verzweifelt, für den ist die Broschüre Finden, was man sucht! von Michael Klems das Richtige. Klems klärt zunächst über Grundsätzliches auf, weist darauf hin, dass die Recherchehilfen bloß Maschinen seien, man ihre oft an Interessen gekoppelten Informationen nicht ungeprüft verwenden solle und ohnehin das Internet nie die einzige Quelle sein dürfe. Interessant sind die konkreten Tipps - etwa zur effizienten Browsernutzung (ein Suchergebnis mit der rechten Maustaste in einem neuen Fenster öffnen; so behält man die Fundliste) oder zu Aufbau und Organisation eines Adressenverzeichnisses. Richtig spannend wird die Broschüre, wenn Klems endlich ins Internet geht. Er erklärt, wie die richtigen Suchbegriffe die Trefferquote erhöhen: Da sich nicht alle Maschinen am Wortstamm orientierten, empfehle es sich, Begriffe sowohl im Singular als auch im Plural einzugeben; außerdem plädiert Klems grundsätzlich für Kleinschreibung. Auch wie Begriffe verknüpft werden, lernt man. Viele Nutzer verlassen sich beim Recherchieren auf Google - und übersehen, dass Webkataloge oder spezielle Suchdienste nützlicher sein können. Klems beschreibt, wann welche Dienste sinnvoll sind: Mit einer Suchmaschine ist man immer auf dem neuesten Stand, während ein Katalog wie Web.de bei der Suche nach bewerteter Information hilft. Mets-Suchmaschinen wie Metager.de sind der Joker - und nur sinnvoll bei Begriffen mit potenziell niedriger Trefferquote. Ebenfalls viel versprechende Anlaufpunkte können die Diskussionsforen des Usenet sein, erreichbar über die Groups-Abfrage bei Google. Wertvoll sind die Tipps für die Literaturrecherche. Eine mehrseitige Linksammlung rundet die Broschüre ab"

RSWK

Internet / Suchmaschine / Ratgeber

Subject

Internet / Suchmaschine / Ratgeber

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RSWK

Internet / Suchmaschine / Navigieren / Leistungsbewertung

Subject

Internet / Suchmaschine / Navigieren / Leistungsbewertung

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RSWK

Suchmaschine / World Wide Web / Information Retrieval

Subject

Suchmaschine / World Wide Web / Information Retrieval