Similarity Search and Applications 16th International Conference, SISAP 2023, A Coruña, Spain, October 9–11, 2023, Proceedings /
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 submissi...
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| Байгууллагын зохиогч: | |
|---|---|
| Бусад зохиолчид: | , |
| Формат: | Цахим Цахим ном |
| Хэл сонгох: | англи |
| Хэвлэсэн: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2023.
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| Хэвлэл: | 1st ed. 2023. |
| Цуврал: | Lecture Notes in Computer Science,
14289 |
| Нөхцлүүд: | |
| Онлайн хандалт: | https://doi.org/10.1007/978-3-031-46994-7 |
| Шошгууд: |
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| 245 | 1 | 0 | |a Similarity Search and Applications |h [electronic resource] : |b 16th International Conference, SISAP 2023, A Coruña, Spain, October 9–11, 2023, Proceedings / |c edited by Oscar Pedreira, Vladimir Estivill-Castro. |
| 250 | |a 1st ed. 2023. | ||
| 264 | 1 | |a Cham : |b Springer Nature Switzerland : |b Imprint: Springer, |c 2023. | |
| 300 | |a 1 online resource (XXI, 310 p. 103 illus.). |b 92 illus. in color. | ||
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| 490 | 1 | |a Lecture Notes in Computer Science, |x 1611-3349 ; |v 14289 | |
| 505 | 0 | |a 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. | |
| 520 | |a 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. | ||
| 588 | |a Print version record. | ||
| 650 | 0 | |a Information storage and retrieval systems. | |
| 650 | 0 | |a Database management. | |
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Application software. | |
| 650 | 1 | 4 | |a Information Storage and Retrieval. |
| 650 | 2 | 4 | |a Database Management. |
| 650 | 2 | 4 | |a Data Mining and Knowledge Discovery. |
| 650 | 2 | 4 | |a Machine Learning. |
| 650 | 2 | 4 | |a Computer and Information Systems Applications. |
| 700 | 1 | |a Pedreira, Oscar. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Estivill-Castro, Vladimir. |e editor. |0 (orcid)0000-0001-7775-0780 |1 https://orcid.org/0000-0001-7775-0780 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
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| 776 | 0 | 8 | |i Printed edition: |z 9783031469930 |
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