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Integrating Prior Knowledge into Deep Learning
2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), 2017Deep learning allows to develop feature representations and train classification models in a fully integrated way. However, learning deep networks is quite hard and it improves over shallow architectures only if a large number of training data is available.
Diligenti M., Roychowdhury S., Gori M.
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Prior Knowledge in the Classification of Biomedical Data
2010Standard data analysis techniques for biomedical problems cannot take into account existing prior knowledge, and available literature results cannot be incorporated in further studies. In this work we review some techniques that incorporate prior knowledge in supervised classification algorithms as constraints to the underlying optimization and linear ...
Abbate Danilo +2 more
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A Knowledge Graph Exploration Method with No Prior Knowledge
Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications, 2022Ariani Indrawati +5 more
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Knowledge Graphs: Opportunities and Challenges
Artificial Intelligence Review, 2023Francesco Osborne, Ciyuan Peng, Feng Xia
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Incorporation of Prior Knowledge
2015The GP modelling framework enables incorporation of prior knowledge of various kinds. The chapter shows the application of GP models in block-oriented nonlinear models, how the local linear dynamic models can be incorporated into GP models and how GP models can be used in the context of the paradigm of linear models with varying parameters.
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A Survey on Knowledge Graphs: Representation, Acquisition, and Applications
IEEE Transactions on Neural Networks and Learning Systems, 2022Pekka Marttinen +2 more
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A review: Knowledge reasoning over knowledge graph
Expert Systems With Applications, 2020Xiaojun Chen
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