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Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Wray Buntine,Marko Grobelnik,John Shawe-Taylor Conference proce

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樓主: invigorating
31#
發(fā)表于 2025-3-26 23:12:08 | 只看該作者
Mining Spatial Co-location Patterns with Dynamic Neighborhood Constrainta greedy algorithm for mining co-location patterns with dynamic neighborhood constraint. The experimental evaluation on a real world data set shows that our algorithm has a better capability than the previous approach on finding co-location patterns together with the consideration of the distribution of data set.
32#
發(fā)表于 2025-3-27 05:05:42 | 只看該作者
Classifier Chains for Multi-label Classificationlexity. Empirical evaluation over a broad range of multi-label datasets with a variety of evaluation metrics demonstrates the competitiveness of our chaining method against related and state-of-the-art methods, both in terms of predictive performance and time complexity.
33#
發(fā)表于 2025-3-27 05:38:53 | 只看該作者
34#
發(fā)表于 2025-3-27 09:40:05 | 只看該作者
Statistical Relational Learning with Formal Ontologiesty relationships can be analyzed via the latent model structure. Second, enforcing the ontological constraints guarantees that the learned model does not predict inconsistent relations. In our experiments, this leads to an improved predictive performance.
35#
發(fā)表于 2025-3-27 14:50:21 | 只看該作者
Integrating Novel Class Detection with Classification for Concept-Drifting Data Streamsg unlabeled test instances, and separation of the test instances from training instances. Our approach is non-parametric, meaning, it does not assume any underlying distributions of data. Comparison with the state-of-the-art stream classification techniques prove the superiority of our approach.
36#
發(fā)表于 2025-3-27 20:22:45 | 只看該作者
Neural Networks for State Evaluation in General Game Playingorementioned problems. A network initialization extracted from the game rules ensures reasonable behavior without the need for prior training. Later training, however, can lead to significant improvements in evaluation quality, as our results indicate.
37#
發(fā)表于 2025-3-27 23:31:44 | 只看該作者
Learning to Disambiguate Search Queries from Short Sessions to predict the user’s intentions and is based on Markov logic, a statistical relational learning model that has been successfully applied to challenging language problems in the past. We present empirical results that demonstrate the effectiveness of our proposed approach on data collected from a commercial general-purpose search engine.
38#
發(fā)表于 2025-3-28 02:10:14 | 只看該作者
39#
發(fā)表于 2025-3-28 10:10:01 | 只看該作者
Efficient Pruning Schemes for Distance-Based Outlier Detectionerhead of the first phase is offset by the reduction in cost of the second phase. We also demonstrate the superiority of our approach over existing distance-based outlier detection methods by extensive empirical studies on real datasets.
40#
發(fā)表于 2025-3-28 13:43:04 | 只看該作者
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