Results 31 to 40 of about 11,467,392 (329)
How Much a Model be Trained by Passive Learning Before Active Learning?
IEEE Access, 2022 Most pool-based active learning studies have focused on query strategy for active learning. In this paper, via empirical analysis on the effect of passive learning before starting active learning, we reveal that the amount of data acquired by passive ...
Dae Ung Jo, Sangdoo Yun, Jin Young Choi
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Active learning with support vector machines [PDF]
WIREs Data Mining and Knowledge Discovery, 2014 In machine learning,active learningrefers to algorithms that autonomously select the data points from which they will learn. There are many data mining applications in which large amounts of unlabeled data are readily available, but labels (e.g., human annotations or results coming from complex experiments) are costly to obtain.
Jan Kremer +2 more
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Deep Active Learning for Computer Vision Tasks: Methodologies, Applications, and Challenges
Applied Sciences, 2022 Active learning is a label-efficient machine learning method that actively selects the most valuable unlabeled samples to annotate. Active learning focuses on achieving the best possible performance while using as few, high-quality sample annotations as ...
Mingfei Wu, Chen Li, Zehuan Yao
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Pairwise meta-rules for better meta-learning-based algorithm ranking [PDF]
, 2013 In this paper, we present a novel meta-feature generation method in the context of meta-learning, which is based on rules that compare the performance of individual base learners in a one-against-one manner.
Pfahringer, Bernhard, Sun, Quan
core +1 more source
Machine learning methods for predicting earthquake frequency in the geographical region surrounding northern Morocco [PDF]
EPJ Web of ConferencesIn order to reduce risks, we utilize machine learning/deep learning models to forecast the frequency of earthquakes in a specific geographic region, such as northern Morocco.
El Hafidi My Ahmed +3 more
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Predicting Active Antimicrobial Compounds Using Machine Learning
Trends in PharmacyBackground: Acinetobacter baumannii is a multidrug-resistant (MDR) pathogen rec ognized by the World Health Organization as a critical priority due to its high preva lence in hospital-acquired infections and limited treatment options.
İbrahim Arman
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Risks, 2018 Advanced machine learning has achieved extraordinary success in recent years. “Active” operational risk beyond ex post analysis of measured-data machine learning could provide help beyond the regime of traditional statistical analysis when it
Udo Milkau, Jürgen Bott
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Active Machine Learning for Formulation of Precision Probiotics.
International journal of pharmaceutics, 2022 It is becoming clear that the human gut microbiome is critical to health and well-being, with increasing evidence demonstrating that dysbiosis can promote disease. Increasingly, precision probiotics are being investigated as investigational drug products
Laura E. McCoubrey +6 more
semanticscholar +1 more source
QuantuMoonLight: A low-code platform to experiment with quantum machine learning
SoftwareX, 2023 Nowadays, machine learning is being used to address multiple problems in various research fields, with software engineering researchers being among the most active users of machine learning mechanisms.
Francesco Amato +18 more
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Machine Learning‐Evolutionary Algorithm Enabled Design for 4D‐Printed Active Composite Structures
Advanced Functional Materials, 2021 Active composites consisting of materials that respond differently to environmental stimuli can transform their shapes. Integrating active composites and 4D printing allows the printed structure to have a pre‐designed complex material or property ...
Xiao-Hao Sun +8 more
semanticscholar +1 more source