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| Titolo: |
Similarity Search and Applications [[electronic resource] ] : 16th International Conference, SISAP 2023, A Coruña, Spain, October 9–11, 2023, Proceedings / / edited by Oscar Pedreira, Vladimir Estivill-Castro
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| Pubblicazione: | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Edizione: | 1st ed. 2023. |
| Descrizione fisica: | 1 online resource (325 pages) |
| Disciplina: | 943.005 |
| Soggetto topico: | Information storage and retrieval systems |
| Database management | |
| Data mining | |
| Machine learning | |
| Application software | |
| Information Storage and Retrieval | |
| Database Management | |
| Data Mining and Knowledge Discovery | |
| Machine Learning | |
| Computer and Information Systems Applications | |
| Persona (resp. second.): | PedreiraOscar |
| Estivill-CastroVladimir | |
| Nota di bibliografia: | Includes bibliographical references and index. |
| Nota di contenuto: | 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-Sharing Tandems 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. |
| Sommario/riassunto: | 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. |
| Titolo autorizzato: | Similarity Search and Applications ![]() |
| ISBN: | 3-031-46994-1 |
| Formato: | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 996558465403316 |
| Lo trovi qui: | Univ. di Salerno |
| Opac: | Controlla la disponibilità qui |