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020			_a9783031178498 _9978-3-031-17849-8
024	7		_a10.1007/978-3-031-17849-8 _2doi
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245	1	0	_aSimilarity Search and Applications _h[electronic resource] : _b15th International Conference, SISAP 2022, Bologna, Italy, October 5-7, 2022, Proceedings / _cedited by Tomáš Skopal, Fabrizio Falchi, Jakub Lokoč, Maria Luisa Sapino, Ilaria Bartolini, Marco Patella.
250			_a1st ed. 2022.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022.
300			_aXI, 305 p. 97 illus., 84 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aLecture Notes in Computer Science, _x1611-3349 ; _v13590
505	0		_aApplications -- Numerical Data Imputation: Choose KNN over Deep Learning -- COSINER: COntext SImilarity data augmentation for Named Entity Recognition -- An Application of Learned Multi-Modal Product Similarity to e-Commerce -- Deep Vision-Language Model for Efficient Multi-modal Similarity Search in Fashion Retrieval -- Stable Anchors for Matching Unlabelled Point Clouds -- Visual Exploration of Human Motion Data -- Foundations -- On Projections to Linear Subspaces -- Concept of Relational Similarity Search -- On the Expected Exclusion Power of Binary Partitions for Metric Search -- Similarity Search with the Distance Density Model -- Generalized Relative Neighborhood Graph (GRNG) for Similarity Search -- A Ptolemaic Partitioning Mechanism -- HubHSP graph: effective data sampling for pivot-based representation strategies -- Indexing and Clustering -- Compacted search tree for graph edit distance computation -- Clustering by Direct Optimization of the Medoid Silhouette -- Automatic Indexing for Similarity Search in ELKI -- Approximate Nearest Neighbor Search on Standard Search Engines -- Evaluation of LID-Aware Graph Embedding Methods for Node Clustering -- Similarity-based Unsupervised Evaluation of Outlier Detection -- Learning -- FastHebb: Scaling Hebbian Training of Deep Neural Networks to ImageNet Level -- Causal Disentanglement with Network Information for Debiased Recommendations -- Causal Disentanglement with Network Information for Debiased Recommendations -- Self-supervised Information Retrieval Trained from Self-generated Sets of Queries and Relevant Documents -- Doctoral Symposium -- Discovering Knowledge Graphs Via Attention-Driven Graph Generation -- Visual Recommendation and Visual Search for Fashion e-commerce.
520			_aThis book constitutes the refereed proceedings of the 15th International Conference on Similarity Search and Applications, SISAP 2022, held in Bologna, Italy in October 2022. SISAP 2022 is an annual international conference for researchers focusing on similarity search challenges and related theoretical/practical problems, as well as the design of content-based similarity search applications. The 15 full papers presented together with 8 short and 2 doctoral symposium papers were carefully reviewed and selected from 34 submissions. They were organized in topical sections as follows: Applications; Foundations; Indexing and Clustering; Learning; Doctoral Symposium.
650		0	_aInformation storage and retrieval systems. _922213
650	1	4	_aInformation Storage and Retrieval. _923927
700	1		_aSkopal, Tomáš. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9175491
700	1		_aFalchi, Fabrizio. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9175492
700	1		_aLokoč, Jakub. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9175493
700	1		_aSapino, Maria Luisa. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9175494
700	1		_aBartolini, Ilaria. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9175495
700	1		_aPatella, Marco. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9175496
710	2		_aSpringerLink (Online service) _9175497
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783031178481
776	0	8	_iPrinted edition: _z9783031178504
830		0	_aLecture Notes in Computer Science, _x1611-3349 ; _v13590 _923263
856	4	0	_uhttps://doi.org/10.1007/978-3-031-17849-8
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