Titlebook: Similarity Search and Applications; 17th International C Edgar Chávez,Benjamin Kimia,Jan Sedmidubsky Conference proceedings 2025 The Editor

圆桶

ETDD70: Eye-Tracking Dataset for Classification of Dyslexia Using AI-Based Methods we demonstrate the utility of such data in training several classification models, the best of which can distinguish between dyslexic and non-dyslexic individuals with an accuracy of around 90 %. Both the dataset and evaluated models provide a valuable resource for researchers to further advance AI-based methods for dyslexia classification.

一回合

Scalable Polyadic Queries.Here, we address these issues. This work demonstrates that the polyadic query mechanism can scale to large data, and gives results which are better than those obtained by executing simple queries over each of the arguments individually.

牢骚

A Dynamic Evaluation Metric for Feature Selectionovide a comparison and analysis to show the different aspects involved in the evaluation of the feature selection algorithms. The results indicate that the proposed metric is successful in carrying out the evaluation task for feature selection algorithms.

GRAVE

Bayesian Estimation Approaches for Local Intrinsic Dimensionalityall number of nearest neighbors and when incorporating informative priors. We also show that in deep neural networks, MLE produces highly volatile LID estimates, whereas a Bayesian approach that incorporates prior LID information smoothes and reduces the variance of these estimates.

anarchist

acrophobia

Identifying Propagating Signals with Spatio-Temporal Clustering in Multivariate Time Seriesby a domain expert. In this paper, we propose a fully unsupervised method to discover propagating signals via density-based clusters with respect to the three aspects mentioned above. For this purpose, we adapt the DBSCAN algorithm to our specific setting and present an exemplary application of this method on pharmacological data.

Alveolar-Bone

Conference proceedings 2025iewed and selected from 32 submissions. They focus on efficient similarity search methods addressing the challenges of exploring similar items and managing vast machine-learning data sets efficiently. .

Baffle

CHARM

An Efficient Framework for Approximate Nearest Neighbor Search on High-Dimensional Multi-metric Dataetric space, we build the same type of proximity graph that allows any search-start node. An approximate NN is found by traversing the proximity graphs while carefully selecting start nodes to avoid redundant node accesses. We conduct experiments on real-world high-dimensional multi-metric data, and

嫌恶