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From IOTA Tangle 2.0 to Rebased: A Comparative Analysis of Decentralization, Scalability, and Suitability for IoT Applications.

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A scoping review of tinnitus research undertaken by New Zealand researchers: Aotearoa-an international hotspot for tinnitus innovation and collaboration.

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A generalisability theory approach to quantifying changes in psychopathology among ultra-high-risk individuals for psychosis.

Schizophrenia (Heidelberg, Germany)·2024

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Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

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相关实验视频

Updated: Jun 30, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

编辑专题集合:"可解释和增强的机器学习用于生物信号和生物医学图像"

Cosimo Ieracitano¹, Mufti Mahmud^2,3,4, Maryam Doborjeh⁵

¹DICEAM Department, University Mediterranea of Reggio Calabria, Via Zehender, Feo di Vito, 89122 Reggio Calabria, Italy.

Sensors (Basel, Switzerland)

|December 23, 2023

概括

此摘要是机器生成的。

机器学习 (ML) 是人工智能 (AI) 的一部分,它使用算法让计算机系统从数据中学习. 这使得系统能够在没有明确编程的情况下提高特定任务的性能.

科学领域:

计算机科学计算机科学
人工智能的人工智能

背景情况:

机器学习 (ML) 是人工智能 (AI) 的一个关键子领域.
它专注于算法和统计模型,使系统能够从数据中学习.
这有助于通过经验自动适应特定任务.

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Last Updated: Jun 30, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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