Results 261 to 270 of about 210,501 (299)
3D Printing Innovations in Polymeric Porous and Patterned Architecture
Advanced Functional Materials, EarlyView.Polymeric foams occupy a unique structural space between dense solids and open networks, where engineered void fraction governs mechanical compliance, thermal resistance, and mass transport. Additive manufacturing now enables precise spatial control over cellular architecture, unlocking designer foam structures across applications spanning crash ...
Dhanush Patil +13 more
wiley +1 more source
Proceedings of the AAAI Conference on Artificial Intelligence, 2018 Earth Mover's Distance (EMD), targeting at measuring the many-to-many distances, has shown its superiority and been widely applied in computer vision tasks, such as object recognition, hyperspectral image classification and gesture recognition.
Zizhao Zhang 0003 +3 more
openaire +2 more sources
, 2015 International audience ; Similarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropriately measure such similarities for a given task is crucial to the performance of many machine learning, pattern recognition and data mining methods. This book is devoted
Bellet, Aurélien +2 more
openaire +3 more sources
Foundations and Trends® in Machine Learning, 2013 The metric learning problem is concerned with learning a distance function tuned to a particular task, and has been shown to be useful when used in conjunction with nearest-neighbor methods and other techniques that rely on distances or similarities. This survey presents an overview of existing research in metric learning, including recent progress on ...
Kulis, Brian
openaire +2 more sources
Deep Metric Learning: A Survey
Symmetry, 2019 Metric learning aims to measure the similarity among samples while using an optimal distance metric for learning tasks. Metric learning methods, which generally use a linear projection, are limited in solving real-world problems demonstrating non-linear ...
Mahmut Kaya
exaly +3 more sources
Some of the next articles are maybe not open access.
Related searches:
Related searches:
2016 International Joint Conference on Neural Networks (IJCNN), 2016
The main theme of this paper is to develop a systematic framework to learn a Mahalanobis distance metric based on matrix sketching. Within this framework, we present a novel sketch metric learning algorithm which sequentially sketches the received samples from training dataset and formulates a new kind of constraint for metric learning.
Yuting Mai +4 more
openaire +1 more source
The main theme of this paper is to develop a systematic framework to learn a Mahalanobis distance metric based on matrix sketching. Within this framework, we present a novel sketch metric learning algorithm which sequentially sketches the received samples from training dataset and formulates a new kind of constraint for metric learning.
Yuting Mai +4 more
openaire +1 more source
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, 2000
We introduce an algorithm for learning a local metric to a continuous input space that measures distances in terms of relevance to the processing task. The relevance is defined as local changes in discrete auxiliary information, which may be for example the class of the data items, an index of performance, or a contextual input.
Samuel Kaski, Janne Sinkkonen
openaire +1 more source
We introduce an algorithm for learning a local metric to a continuous input space that measures distances in terms of relevance to the processing task. The relevance is defined as local changes in discrete auxiliary information, which may be for example the class of the data items, an index of performance, or a contextual input.
Samuel Kaski, Janne Sinkkonen
openaire +1 more source
Reinforcement learning based metric filtering for evolutionary distance metric learning
Intelligent Data Analysis, 2020Data collection plays an important role in business agility; data can prove valuable and provide insights for important features. However, conventional data collection methods can be costly and time-consuming. This paper proposes a hybrid system R-EDML that combines a sequential feature selection performed by Reinforcement Learning (RL) with the ...
Bassel Ali +4 more
openaire +1 more source
Boundary-restricted metric learning
Machine Learning, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shuo Chen 0003 +5 more
openaire +2 more sources