As a result, our approach learns not only nonlinear metric that inherits the flexibility of GP but also representative features for the follow-up tasks.
As a result, our approach learns not only nonlinear metric that inherits the flexibility of GP but also representative features for the follow-up tasks.
As a result, our approach learns not only nonlinear metric that inherits the flexibility of GP but also representative features for the follow-up tasks.
Abstract: In this paper, a statistical machine learning approach for constructing a metric separating unseen writer hands, is proposed.
As a result, our approach learns not only nonlinear metric that inherits the flexibility of GP but also representative features for the follow-up tasks.
GPML belongs to both the gradient-based meta-learning approach, which searches for an initializer for a new task, and the metric- based meta-learning approach ...
Abstract: In this paper, a statistical machine learning approach for constructing a metric separating unseen writer hands, is proposed.
Ping Li, Songcan Chen: Gaussian process approach for metric learning. Pattern Recognit. 87: 17-28 (2019). manage site settings. To protect your privacy, ...
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for Forensic Writer Identification ; jf.wahlberg@gmail.com ; —In this paper, a statistical machine learning ; approach for constructing a metric separating unseen ...
Aug 24, 2021 · Domain Decomposition Approach for Fast Gaussian Process Regression of Large Spatial Data Sets. Journal of Machine Learning Research. 2011;12 ...