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Machine learning (ML) models, including rule-based architectures dominant in the technology of fuzzy sets, are constructed with the aid of intensive usage of data. Quite often, knowledge about the system/problem is available and offers some tangible benefits to the construction of the models. The relevance of knowledge...
Multi-View Multi-Label Learning is pivotal for visual content understanding yet faces two fundamental bottlenecks: (i) Structural Decoupling, where prevalent matrix flattening severs intrinsic high-order correlations between visual features and semantic concepts; and (ii) Semantic Uniformity Bias, where the neglect of...
This work introduces a novel quantum incremental learning framework based on trainable mixed-state prototypes that achieves high-dimensional feature concentration using a minimal number of qubits, while demonstrating lower computational complexity and robust representation in incremental learning tasks compared with cl...
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