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Produktbild: Similarity Search and Applications
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Similarity Search and Applications 19th International Conference, SISAP 2026, Brno, Czech Republic, October 14–16, 2026, Proceedings

Aus der Reihe Lecture Notes in Computer Science

81,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

14.11.2026

Abbildungen

XXVI, 490 p. 132 illus., 116 illus. in color.

Herausgeber

Klaus Schoeffmann + weitere

Verlag

Springer

Seitenzahl

490

Maße (B/H)

15,5/23,5 cm

Sprache

Englisch

ISBN

978-3-032-42223-1

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

14.11.2026

Abbildungen

XXVI, 490 p. 132 illus., 116 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

490

Maße (B/H)

15,5/23,5 cm

Sprache

Englisch

ISBN

978-3-032-42223-1

Herstelleradresse

Springer International Publishing AG
Gewerbestr. 11
6330 Cham
Schweiz
Url: www.springer.com

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  • Produktbild: Similarity Search and Applications

  • .- Graph-Based Similarity Search.

    .- Polytopes, Bits and Hashes: Space-Efficient Construction for NN Graphs.

    .- Calibrated Early Termination: Dataset-Independent PAC Bounds for Graph-Based Nearest Neighbor Search.

    .- Simple and Fast Algorithm for Graph-based Filtered Approximate Nearest Neighbor Search.

    .- Needle in a Knowledge Graph: Generating Scalable Long-Context Benchmarks for KG Question Answering.

    .- Indexing, Hashing and Sketches.

    .- A Detailed Analysis of MaxGeomHash Sketches.

    .- Cyclical Query Update: A Novel Query Algorithm for Locality-Sensitivity Based Similarity Search.

    .- Fast Construction of Learned Count-Min Sketch via Ternary Search.

    .- KTree: a Kernel-Based Index for Scalable Exact Similarity Search.

    .- Theory and Principles of Similarity Search.

    .- Nearest Neighbors Need Better Neighborhoods (Position).

    .- Closeness, a natural notion of proximity.

    .- Towards Truly Unsupervised Evaluation of Feature Selection.

    .- Local Intrinsic Dimensionality and Lyapunov Exponents in Fixed-Point Iteration.

    .- Barycentric Charts for Geodesic Queries on Compressed Indices.

    .- Feature Selection Granularity: An Empirical Study.

    .- Clustering and Anomaly Detection.

    .- Limits of Clustering Models Based on Distance Similarity: A Sensitivity Study of SDOclust.

    .- ADoCL: Anomaly-Aware Dynamic Concept Maintenance for Reliable Continual Learning.

    .- Clustering Validation with The Area Under Precision-Recall Curve.

    .- Capacity-Aware Cluster Rebalancing for Efficient R-Tree Indexing.

    .- Sequence Search and Reranking

    .- Extending Drop-DTW for clustering multivariate timed sequences.

    .- QSet-Rank: Dispersion-Aware Question-Set Reranking for Robust Biomedical RAG.

    .- NOOPS: Nonmyopic Time Series Pattern Search.

    .- Top-k on Sequences : A Generic Approach for Similarity Search.

    .- Search Parametrization and Personalization.

    .- Approximate Selection of Best Matching Unit for Large-Scale Self-Organizing Maps.

    .- CORA: Coreset-Based Probabilistic Stopping in Approximate Similarity Search.

    .- Interactive Personalised Image Search for Vector Databases.

    .- Doctoral Symposium.

    .- Querying Polyobjects.

    .- Approximate Nearest Neighbor Search over Distance Distributions in Probabilistic Metric Space.

    .- Curvature and border effect on dimensionality estimation.

    .- Indexing Challenge.

    .- Overview of the SISAP 2026 Indexing Challenge: k-NN Graphs, LLM Workloads, and Sparse Embeddings.

    .- InvertedBlock Indexing for Learned Sparse MIPS.

    .- Hardware-Sympathetic Optimization for High-Dimensional Vector Search 348

    .- Crusty Coders SISAP Challenge 2026.

    .- PiPNN for k-NN Graph Construction: Inverse-Size Merging and Diversified Beam Search.

    .- Fast and Efficient Approximate Nearest Neighbor Search for High-Dimensional LLM Embeddings.

    .- SISAP 2026 Indexing Challenge: Plain Local Search.

    .- Two Indexes, One Foundation: kANNolo and Seismic for Efficient Learned Sparse Retrieval at SISAP 2026.

    .- Learned k-NN Graph Construction and Spherical Indexing for MIPS.

    .- Fast k-NN Graph Building with Quantized PiPNN and Filtering.

    .- NMF-IVF: Learning Latent Semantic Topics for Approximate Retrieval.

    .- Memory-Bounded Approximate Nearest Neighbour Search for Dense, MIPS, and Sparse Vectors: SISAP 2026 Indexing Challenge.

    .- Maximum Inner Product Search with Decoupled Construction- and Query-Measures.

    .- SISAP Indexing Challenge 2026 Solution for Task 1: Accelerating NNDescent with LSH.