PPT-Hinrich

Author : min-jolicoeur | Published Date : 2016-03-03

Schütze and Christina Lioma Lecture 14 Vector Space Classification 1 Overview Recap Feature selection Intro vector space classification Rocchio kNN Linear

Presentation Embed Code

Download Presentation

Download Presentation The PPT/PDF document "Hinrich" is the property of its rightful owner. Permission is granted to download and print the materials on this website for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.

Hinrich: Transcript

Schütze and Christina Lioma Lecture 14 Vector Space Classification 1 Overview Recap Feature selection Intro vector space classification Rocchio kNN Linear classifiers. V 2007 Abstract The colors of fruits and 64258owers are traditionally viewed as an adaptation to increase the detectability of plant organs to animal vectors The detectability of visual signals increases with increasing contrasts between target and b . Schütze. and Christina . Lioma. Lecture 3: Dictionaries and tolerant retrieval. 1. Overview. Recap . . Dictionaries. . Wildcard queries. Edit distance. Spelling correction. Soundex. 2. Outline. . Schütze. and Christina . Lioma. Lecture . 20: Crawling. 1. Overview. . R. ecap . . A simple crawler. . A real crawler. 2. Outline. . R. ecap . . A simple crawler. . A real crawler. 3. 4. Search. . Schütze. and Christina . Lioma. Lecture . 11: Probabilistic Information Retrieval. 1. Overview. . Probabilistic Approach to Retrieval. . Basic Probability Theory. Probability Ranking Principle. . Schütze. and Christina . Lioma. Lecture 7: Scores in a Complete Search System. 1. Overview. Recap . . Why rank? . More on cosine. Implementation of ranking . The complete search system. 2. . Schütze. and Christina . Lioma. Lecture . 15-2: Learning to Rank. 1. Overview. . Learning . Boolen. Weights. . Learning Real-Valued Weights. Rank Learning as Ordinal Regression. 2. Outline. . . Schütze. and Christina . Lioma. Lecture 5: Index Compression. 1. Overview. Recap . . Compression. . Term statistics. Dictionary compression. Postings compression. 2. Outline. Recap . . Compression. . Schütze. and Christina . Lioma. Lecture 9: Relevance Feedback & Query Expansion. 1. 2. Take-. away. . today. Interactive relevance feedback:. improve initial retrieval results by telling the IR system which docs are relevant / . . Schütze. and Christina . Lioma. Lecture 2: The term vocabulary and postings lists. 1. Overview. Recap . . Documents. . Terms. General + Non-English. English. Skip pointers. Phrase queries. 2. . Schütze. and Christina . Lioma. Lecture . 19: Web Search. 1. Overview. Recap . . Big picture. Ads . Duplicate detection. 2. Outline. Recap . . Big picture. Ads . Duplicate detection. 3. 4. Lioma. Lecture 5: Index Compression. 1. Overview. Recap . . Compression. . Term statistics. Dictionary compression. Postings compression. 2. Outline. Recap . . Compression. Term statistics. Dictionary compression. Lioma. Lecture 3: Dictionaries and tolerant retrieval. 1. Overview. Recap . . Dictionaries. . Wildcard queries. Edit distance. Spelling correction. Soundex. 2. Outline. Recap . . Dictionaries. Wildcard queries. Lioma. Lecture . 20: Crawling. 1. Overview. . R. ecap . . A simple crawler. . A real crawler. 2. Outline. . R. ecap . . A simple crawler. . A real crawler. 3. 4. Search. . engines. rank . content. Lioma. Lecture . 18: Latent Semantic Indexing. 1. Overview. Latent semantic indexing . Dimensionality reduction. LSI in information retrieval. 2. Outline. Latent semantic indexing . Dimensionality reduction.

Download Document

Here is the link to download the presentation.
"Hinrich"The content belongs to its owner. You may download and print it for personal use, without modification, and keep all copyright notices. By downloading, you agree to these terms.