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Titlebook: Structural, Syntactic, and Statistical Pattern Recognition; Joint IAPR Internati Antonio Robles-Kelly,Marco Loog,Richard Wilson Conference

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樓主: 自治
21#
發(fā)表于 2025-3-25 06:40:00 | 只看該作者
GriMa: A Grid Mining Algorithm for Bag-of-Grid-Based Classificationy based on costly isomorphism tests and countless expansion possibilities. In this paper, we explain how to exploit grid-based representations of problems to efficiently extract frequent grid subgraphs and create Bag-of-Grids which can be used as new features for classification purposes. We provide
22#
發(fā)表于 2025-3-25 09:25:10 | 只看該作者
Edge Centrality via the Holevo Quantityt of measuring the centrality of an edge. In this paper, we propose a novel edge centrality index rooted in quantum information. More specifically, we measure the importance of an edge in terms of the contribution that it gives to the Von Neumann entropy of the graph. We show that this can be comput
23#
發(fā)表于 2025-3-25 12:51:15 | 只看該作者
Thermodynamic Network Analysis with Quantum Spin Statisticscupation of energy levels defined by the network. We commence from the set of energy states given by the eigenvalues of the normalized Laplacian matrix, which plays the role of the Hamiltonian operator of the network. We explore a heat bath analogy in which the network is in thermodynamic equilibriu
24#
發(fā)表于 2025-3-25 18:57:41 | 只看該作者
Correlation Network Evolution Using Mean Reversion Autoregression stock market data. The work is motivated by the assumption that the price and return of a stock eventually regresses back towards their mean or average. This allows us to model the stock correlation time-series as an autoregressive process with a mean reversion term. Traditionally, the mean is comp
25#
發(fā)表于 2025-3-25 23:41:54 | 只看該作者
26#
發(fā)表于 2025-3-26 00:32:34 | 只看該作者
Least-Squares Regression with Unitary Constraints for Network Behaviour Classificationideration. We show the effectiveness of our approach as compared to alternatives elsewhere in the literature for classification on synthetic data and network behaviour log data, where we present results on attack identification and network status prediction.
27#
發(fā)表于 2025-3-26 06:50:19 | 只看該作者
A Multi-Stage Approach for Fast Person Re-identification propose a multi-stage ranking approach to attain a trade-off with ranking quality, for any . descriptor. We give a preliminary evaluation of our approach on the benchmark VIPeR data set, using different state-of-the-art descriptors.
28#
發(fā)表于 2025-3-26 11:13:29 | 只看該作者
29#
發(fā)表于 2025-3-26 14:22:16 | 只看該作者
30#
發(fā)表于 2025-3-26 18:49:30 | 只看該作者
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