Results 21 to 30 of about 1,532,491 (274)
Learning Heterogeneous Similarity Measures for Hybrid-Recommendations in Meta-Mining [PDF]
, 2012 The notion of meta-mining has appeared recently and extends the traditional meta-learning in two ways. First it does not learn meta-models that provide support only for the learning algorithm selection task but ones that support the whole data-mining ...
Hilario, Melanie +3 more
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Scale Information Enhancement for Few-Shot Object Detection on Remote Sensing Images
Remote Sensing, 2023 Recently, deep learning-based object detection techniques have arisen alongside time-consuming training and data collection challenges. Although few-shot learning techniques can boost models with few samples to lighten the training load, these approaches
Zhenyu Yang +4 more
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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 +2 more sources
, 2013 In: Encyclopedia of Systems Biology, W. Dubitzky, O. Wolkenhauer, K-H Cho, H. Yokota (Eds.), Springer 2011 Meta-learning methods are aimed at automatic discovery of interesting models of data. They belong to a branch of Machine Learning that tries to replace human experts involved in the Data Mining process of creating various computational models ...
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Deep Speaker Recognition: Process, Progress, and Challenges
IEEE Access, 2021 Speaker recognition is related to human biometrics dealing with the identification of speakers from their speech. Speaker recognition is an active research area and being widely investigated using artificially intelligent mechanisms.
Abu Quwsar Ohi +3 more
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Picking groups instead of samples: a close look at Static Pool-based Meta-Active Learning [PDF]
, 2019 ©2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new ...
Mas Méndez, Ignasi +2 more
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Meta-features for meta-learning
Knowledge-Based Systems, 2022 Meta-learning is increasingly used to support the recommendation of machine learning algorithms and their configurations. These recommendations are made based on meta-data, consisting of performance evaluations of algorithms and characterizations on prior datasets.
Adriano Rivolli +4 more
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IEEE Access, 2022 NAND flash memory is becoming smaller and denser to have a larger storage capacity as technologies related to fine processes are developed. As a side effect of high-density integration, the memory can be vulnerable to circuit-level noise such as random ...
Minyoung Hwang +5 more
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Meta-learned models of cognition
Behavioral and Brain Sciences, 2023 Abstract Psychologists and neuroscientists extensively rely on computational models for studying and analyzing the human mind. Traditionally, such computational models have been hand-designed by expert researchers. Two prominent examples are cognitive architectures and Bayesian models of cognition. Although the former requires the specification of a
Marcel Binz +5 more
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, 2019 Meta-learning, or learning to learn, is the science of systematically observing how different machine learning approaches perform on a wide range of learning tasks, and then learning from this experience, or meta-data, to learn new tasks much faster than otherwise possible.
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