{"product_id":"9783030609351","title":"Similarity Search and Applications : 13th International Conference, SISAP 2020, Copenhagen, Denmark, September 30 - October 2, 2020, Proceedings (Lecture Notes in Computer Science 12440) (1st ed. 2020 2020 xix, 414 S. 106 SW-Abb., 104 Farbabb. 235 mm)","description":"This book constitutes the refereed proceedings of the 13th International Conference on Similarity Search and Applications, SISAP 2020, held in Copenhagen, Denmark, in September\/October 2020. The conference was held virtually due to the COVID-19 pandemic.\u003cp\u003eThe 19 full papers presented together with 12 short and 2 doctoral symposium papers were carefully reviewed and selected from 50 submissions. The papers are organized in topical sections named: scalable similarity search; similarity measures, search, and indexing; high-dimensional data and intrinsic dimensionality; clustering; artificial intelligence and similarity; demo and position papers; and doctoral symposium.\u003c\/p\u003e \u003cb\u003eScalable Similarity Search.- \u003c\/b\u003eAccelerating Metric Filtering by Improving Bounds on Estimated Distances.- Differentially Private Sketches for Jaccard Similarity Estimation.- Pivot Selection for Narrow Sketches by Optimization Algorithms.- mmLSH: A Practical and Efficient Technique for Processing Approximate Nearest Neighbor Queries on Multimedia Data.- Parallelizing Filter-Verification based Exact Set Similarity Joins on Multicores.- Similarity Search with Tensor Core Units.- On the Problem of p1 in Locality-Sensitive Hashing.- \u003cb\u003eSimilarity Measures, Search, and Indexing.- \u003c\/b\u003eConfirmation Sampling for Exact Nearest Neighbor Search.- Optimal Metric Search Is Equivalent to the Minimum Dominating Set Problem.- Metrics and Ambits and Sprawls, Oh My: Another Tutorial on Metric Indexing.- Some branches may bear rotten fruits: Diversity browsing VP-Trees.- Continuous Similarity Search for Evolving Database.- Taking advantage of highly-correlated attributes in similarity queries with missing values.- Similarity Between Points in Metric Measure Spaces.- \u003cb\u003eHigh-dimensional Data and Intrinsic Dimensionality.- \u003c\/b\u003eGTT: Guiding the Tensor Train Decomposition.- Noise Adaptive Tensor Train Decomposition for Low-Rank Embedding of Noisy Data.- ABID: Angle Based Intrinsic Dimensionality.- Sampled Angles in High-Dimensional Spaces.- Local Intrinsic Dimensionality III: Density and Similarity.- Analysing Indexability of Intrinsically High-dimensional Data using TriGen.- Reverse k-Nearest Neighbors Centrality Measures and Local Intrinsic Dimension.- \u003cb\u003eClustering.- \u003c\/b\u003eBETULA: Numerically Stable CF-Trees for BIRCH Clustering.- Using a Set of Triangle Inequalities to Accelerate K-means Clustering.- Angle-Based Clustering.- \u003cb\u003eArtificial Intelligence and Similarity.- \u003c\/b\u003eImproving Locality Sensitive Hashing by Efficiently Finding Projected Nearest Neighbors.- SIR: Similar Image Retrieval for Product Search in E-Commerce.- Cross-Resolution deep features based Image Search.- LearningDistance Estimators from Pivoted Embeddings of Metric Objects.- \u003cb\u003eDemo and Position Papers.- \u003c\/b\u003eVisualizer of Dataset Similarity using Knowledge Graph.- vitrivr-explore: Guided Multimedia Collection Exploration for Ad-hoc Video Search.- Running experiments with confidence and sanity.- \u003cb\u003eDoctoral Symposium.- \u003c\/b\u003eTemporal Similarity of Trajectories in Graphs.- Relational Visual-Textual Information Retrieval.","brand":"SPRINGER, BERLIN; SPRINGER INTERNATIONAL PUBLISHING;","offers":[{"title":"Default Title","offer_id":48820204437739,"sku":"00000_00000_00000_00000","price":183.1,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0758\/4484\/5803\/files\/9783030609351-1.jpg?v=1781716963","url":"https:\/\/kinokuniya.com.sg\/zh\/products\/9783030609351","provider":"Books Kinokuniya Singapore","version":"1.0","type":"link"}