☆ 3.8 Proceedings Paper

Dimensionality Reduction to Dynamically Reduce Data

2022 IEEE PHYSICAL ASSURANCE AND INSPECTION OF ELECTRONICS (PAINE) (2022)

A Survey on Data Collection for Machine Learning: A Big Data-AI Integration Perspective

Yuji Roh et al.

Summary: Data collection is a major bottleneck in machine learning due to the lack of labeled data in new applications and the feature generation process in deep learning requiring more labeled data. Research in data collection comes from machine learning, natural language, and computer vision communities, as well as data management due to the importance of handling large amounts of data. The integration of machine learning and data management opens many opportunities for new research in the context of Big Data and Artificial Intelligence (AI) integration.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2021)

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Proceedings Paper Computer Science, Artificial Intelligence

Balanced-MixUp for Highly Imbalanced Medical Image Classification

Adrian Galdran et al.

Summary: In this paper, a novel mechanism called Balanced-MixUp is proposed to address highly imbalanced medical image classification problems, it improves the balance of training data by simultaneously performing regular and balanced sampling, and experiments show promising results compared to traditional approaches.

MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT V (2021)

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Article Chemistry, Physical

Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures

Yashar Kiarashinejad et al.

NPJ COMPUTATIONAL MATERIALS (2020)

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Article Ophthalmology

Supervised learning and dimension reduction techniques for quantification of retinal fluid in optical coherence tomography images

A. Breger et al.

EYE (2017)

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Article Chemistry, Multidisciplinary

Experimental Demonstration of Feature Extraction and Dimensionality Reduction Using Memristor Networks

Shinhyun Choi et al.

NANO LETTERS (2017)

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Dimensionality Reduction to Dynamically Reduce Data

相关参考文献

A Survey on Data Collection for Machine Learning: A Big Data-AI Integration Perspective

Balanced-MixUp for Highly Imbalanced Medical Image Classification

Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures

Supervised learning and dimension reduction techniques for quantification of retinal fluid in optical coherence tomography images

Experimental Demonstration of Feature Extraction and Dimensionality Reduction Using Memristor Networks

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