fachmedien.de - spezialisierte Fachinformationen für Studium und Beruf
fachmedien.de - spezialisierte Fachinformationen für Studium und Beruf
fachmedien.de - spezialisierte Fachinformationen für Studium und Beruf
fachmedien.de - spezialisierte Fachinformationen für Studium und Beruf
fachmedien.de - spezialisierte Fachinformationen für Studium und Beruf

Site Breadcrumb

Verkauf durch Sack Fachmedien

Estivill-Castro / Pedreira

Similarity Search and Applications

16th International Conference, SISAP 2023, A Coruña, Spain, October 9-11, 2023, Proceedings

Medium: Buch
ISBN: 978-3-031-46993-0
Verlag: Springer Nature Switzerland
Erscheinungstermin: 27.10.2023
In den Warenkorb
Lieferfrist: bis zu 10 Tage
Auf die Merkliste Drucken
Leseprobe
Überschrift

Beschreibung

Kostenloser Versand

nach Deutschland, Österreich und in die Schweiz

Bequem zahlen

Rechnung, Kreditkarte, PayPal oder Bankeinzug

Wir sind gerne für Sie da

0800 000-1378 (Inland)
service@fachmedien.de
<<Kontaktformular>>

Fragen

Fragen Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua
Anwtorten

Anwtorten At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet

This book constitutes the refereed proceedings of the 16th International Conference on Similarity Search and Applications, SISAP 2023, held in A Coruña, Spain, during October 9–11, 2023.
The 16 full papers and 4 short papers included in this book were carefully reviewed and selected from 33 submissions. They were organized in topical sections as follows: similarity queries, similarity measures, indexing and retrieval, data management, feature extraction, intrinsic dimensionality, efficient algorithms, similarity in machine learning and data mining.

Produkteigenschaften

  • Artikelnummer: 9783031469930
  • Medium: Buch
  • ISBN: 978-3-031-46993-0
  • Verlag: Springer Nature Switzerland
  • Erscheinungstermin: 27.10.2023
  • Sprache(n): Englisch
  • Auflage: 1. Auflage 2023
  • Serie: Lecture Notes in Computer Science
  • Produktform: Kartoniert
  • Gewicht: 505 g
  • Seiten: 310
  • Format (B x H x T): 155 x 235 x 19 mm
  • Ausgabetyp: Kein, Unbekannt

Themen

Autoren/Hrsg.

Herausgeber

Keynotes.- From Intrinsic Dimensionality to Chaos and Control: Towards a Unified Theoretical View.- The Rise of HNSW: Understanding Key Factors Driving the Adoption.- Towards a Universal Similarity Function: the Information Contrast Model and its Application as Evaluation Metric in Artificial Intelligence Tasks.- Research Track.- Finding HSP Neighbors via an Exact, Hierarchical Approach.- Approximate Similarity Search for Time Series Data Enhanced by Section Min-Hash.- Mutual nearest neighbor graph for data analysis: Application to metric space clustering.- An Alternating Optimization Scheme for Binary Sketches for Cosine Similarity Search.- Unbiased Similarity Estimators using Samples.- Retrieve-and-Rank End-to-End Summarization of Biomedical Studies.- Fine-grained Categorization of Mobile Applications through Semantic Similarity Techniques for Apps Classification.- Runs of Side-SharingTandems in Rectangular Arrays.- Turbo Scan: Fast Sequential Nearest Neighbor Search in High Dimensions.- Class Representatives Selection in Non-Metric Spaces for Nearest Prototype Classification.- The Dataset-similarity-based Approach to Select Datasets for Evaluation in Similarity Retrieval.- Suitability of Nearest Neighbour Indexes for Multimedia Relevance Feedback.- Accelerating k-Means Clustering with Cover Trees.- Is Quantized ANN Search Cursed? Case Study of Quantifying Search and Index Quality.- Minwise-Independent Permutations with Insertion and Deletion of Features.- SDOclust: Clustering with Sparse Data Observers.- Solving k-Closest Pairs in High-Dimensional Data using Locality- Sensitive Hashing.- Vec2Doc: Transforming Dense Vectors into Sparse Representations for Efficient Information Retrieval.- Similarity Search with Multiple-Object Queries.- Diversity Similarity Join for Big Data.- Indexing Challenge.- Overview of the SISAP 2023 Indexing Challenge.- Enhancing Approximate Nearest Neighbor Search: Binary-Indexed LSH-Tries, Trie Rebuilding, And Batch Extraction.- General and Practical Tuning Method for Off-the-Shelf Graph-Based Index: SISAP Indexing Challenge Report by Team UTokyo.- SISAP 2023 Indexing Challenge – Learned Metric Index.- Computational Enhancements of HNSW Targeted to Very Large Datasets.- CRANBERRY: Memory-Effective Search in 100M High-Dimensional CLIP Vectors.