Results 41 to 50 of about 4,317,504 (301)
Multi-Label Classification With Hyperdimensional Representations
IEEE Access, 2023 Hyperdimensional computing (HDC) is a computational paradigm that leverages the mathematical properties of high-dimensional vector spaces to manipulate data as symbolic entities using a set of neurally plausible operations. Although HDC has demonstrated remarkable success in cognitive tasks, its potential in complex applications such as multi-label ...
Rishikanth Chandrasekaran +3 more
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From sequence to enzyme mechanism using multi-label machine learning [PDF]
, 2014 Background: In this work we predict enzyme function at the level of chemical mechanism, providing a finer granularity of annotation than traditional Enzyme Commission (EC) classes.
De Ferrari, Luna; id_orcid +5 more
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Multi-label literature classification based on the Gene Ontology graph
BMC Bioinformatics, 2008 Background The Gene Ontology is a controlled vocabulary for representing knowledge related to genes and proteins in a computable form. The current effort of manually annotating proteins with the Gene Ontology is outpaced by the rate of accumulation of ...
Lu Xinghua +3 more
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Cross-modal multi-label image classification modeling and recognition based on nonlinear
Nonlinear Engineering, 2023 Recently, it has become a popular strategy in multi-label image recognition to predict those labels that co-occur in a picture. Previous work has concentrated on capturing label correlation but has neglected to correctly fuse picture features and label ...
Yuan Shuping +5 more
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Learning multi-label scene classification [PDF]
Pattern Recognition, 2004 In classic pattern recognition problems, classes are mutually exclusive by definition. Classification errors occur when the classes overlap in the feature space. We examine a different situation, occurring when the classes are, by definition, not mutually exclusive.
Matthew R. Boutell +3 more
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An evolutionary decomposition-based multi-objective feature selection for multi-label classification [PDF]
PeerJ Computer Science, 2020 Data classification is a fundamental task in data mining. Within this field, the classification of multi-labeled data has been seriously considered in recent years. In such problems, each data entity can simultaneously belong to several categories. Multi-
Azam Asilian Bidgoli +2 more
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MlTr: Multi-label Classification with Transformer
CoRR, 2021 The task of multi-label image classification is to recognize all the object labels presented in an image. Though advancing for years, small objects, similar objects and objects with high conditional probability are still the main bottlenecks of previous convolutional neural network(CNN) based models, limited by convolutional kernels' representational ...
Xing Cheng +7 more
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A Novel IGBT Health Evaluation Method Based on Multi-Label Classification
IEEE Access, 2019 The IGBT health evaluation of power semiconductor devices is usually based on the threshold evaluation method, which is usually a single characteristic parameter evaluation system. This kind of evaluation method cannot reflect the internal correlation of
Ruikun Quan, Hui Li, Yaogang Hu, Pei Gao
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Deep Learning for Multi-label Classification
CoRR, 2014 In multi-label classification, the main focus has been to develop ways of learning the underlying dependencies between labels, and to take advantage of this at classification time. Developing better feature-space representations has been predominantly employed to reduce complexity, e.g., by eliminating non-helpful feature attributes from the input ...
Jesse Read, Fernando Pérez-Cruz
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Deep Multi-label Classification in Affine Subspaces [PDF]
, 2019 Multi-label classification (MLC) problems are becoming increasingly popular in the context of medical imaging. This has in part been driven by the fact that acquiring annotations for MLC is far less burdensome than for semantic segmentation and yet ...
Sznitman, Raphael +7 more
core +1 more source