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Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Toon Calders,Floriana Esposito,Rosa Meo Conference proceedings

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51#
發(fā)表于 2025-3-30 11:29:34 | 只看該作者
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發(fā)表于 2025-3-30 15:22:49 | 只看該作者
Link Prediction in Multi-modal Social Networks majority of earlier work in link prediction infers new interactions between users by mainly focusing on a single network type. However, users also form several . social networks through their daily interactions like commenting on people’s posts or rating similarly the same products. Prior work prim
53#
發(fā)表于 2025-3-30 16:38:51 | 只看該作者
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發(fā)表于 2025-3-30 21:13:24 | 只看該作者
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發(fā)表于 2025-3-31 03:06:21 | 只看該作者
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發(fā)表于 2025-3-31 08:46:32 | 只看該作者
Integer Bayesian Network Classifiers. These networks allow for efficient implementation in hardware while maintaining a (partial) probabilistic interpretation under scaling. An algorithm for the computation of margin maximizing integer parameters is presented and its efficiency is demonstrated. The resulting parameters have superior c
57#
發(fā)表于 2025-3-31 10:10:23 | 只看該作者
58#
發(fā)表于 2025-3-31 16:28:22 | 只看該作者
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發(fā)表于 2025-3-31 19:48:30 | 只看該作者
60#
發(fā)表于 2025-3-31 21:40:29 | 只看該作者
On Learning Matrices with Orthogonal Columns or Disjoint Supportssed a strictly convex matrix norm for orthogonal transfer. We show that this norm converges to a particular atomic norm when its convexity parameter decreases, leading to new algorithmic solutions to minimize it. We also investigate concave formulations of this norm, corresponding to more aggressive
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