8 citations · 30 across the 5 of their papers we have counts for
6 papers
Generalized Determinantal Point Processes for Attraction and Repulsion with Distinct Interaction Kernels
Hideaki Ishibashi, Takahiro Kawashima, Hideitsu Hino
Determinantal point processes (DPPs) model subset selection probability with repulsion, but many applications also exhibit attraction arising from pairwise relations or shared late…
Multi-task manifold learning for small sample size datasets
Hideaki Ishibashi, Kazushi Higa, Tetsuo Furukawa
In this study, we develop a method for multi-task manifold learning. The method aims to improve the performance of manifold learning for multiple tasks, particularly when each task…
Principal component analysis for Gaussian process posteriors
Hideaki Ishibashi, Shotaro Akaho
This paper proposes an extension of principal component analysis for Gaussian process (GP) posteriors, denoted by GP-PCA. Since GP-PCA estimates a low-dimensional space of GP poste…
Stopping Criterion for Active Learning Based on Error Stability
Hideaki Ishibashi, Hideitsu Hino
Active learning is a framework for supervised learning to improve the predictive performance by adaptively annotating a small number of samples. To realize efficient active learnin…
Visual analytics of set data for knowledge discovery and member selection support
Ryuji Watanabe, Hideaki Ishibashi, Tetsuo Furukawa
Visual analytics (VA) is a visually assisted exploratory analysis approach in which knowledge discovery is executed interactively between the user and system in a human-centered ma…
Stopping criterion for active learning based on deterministic generalization bounds
Hideaki Ishibashi, Hideitsu Hino
Active learning is a framework in which the learning machine can select the samples to be used for training. This technique is promising, particularly when the cost of data acquisi…