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Titlebook: Knowledge Discovery in Databases: PKDD 2003; 7th European Confere Nada Lavra?,Dragan Gamberger,Hendrik Blockeel Conference proceedings 2003

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11#
發(fā)表于 2025-3-23 13:14:56 | 只看該作者
Mr-SBC: A Multi-relational Na?ve Bayes Classifierred in several tables related by foreign key constraints and each example is represented by a set of related tuples rather than a single row as in the classical data mining setting. This work is characterized by three aspects. First, an integrated approach in the computation of the posterior probabi
12#
發(fā)表于 2025-3-23 17:09:42 | 只看該作者
SMOTEBoost: Improving Prediction of the Minority Class in Boostingnority (or interesting) class usually produces biased classifiers that have a higher predictive accuracy over the majority class(es), but poorer predictive accuracy over the minority class. SMOTE (Synthetic Minority Over-sampling TEchnique) is specifically designed for learning from imbalanced data
13#
發(fā)表于 2025-3-23 18:25:38 | 只看該作者
Using Belief Networks and Fisher Kernels for Structured Document Classifications to simultaneously take into account structure and content information. We then show how this model can be extended into a more efficient classifier using the Fisher kernel method. In both cases model parameters are learned from a labelled training set of representative documents. We present experi
14#
發(fā)表于 2025-3-23 23:18:48 | 只看該作者
A Skeleton-Based Approach to Learning Bayesian Networks from Dataes the main advantages of these algorithms yet avoids their difficulties. In our approach, first an undirected graph, termed the ., is constructed from the data, using zero- and first-order dependence tests. Then, a search algorithm is employed that builds upon a quality measure to find the best net
15#
發(fā)表于 2025-3-24 04:12:45 | 只看該作者
16#
發(fā)表于 2025-3-24 09:51:08 | 只看該作者
17#
發(fā)表于 2025-3-24 11:46:57 | 只看該作者
Visualizing Class Probability Estimatorsto the users is how to gain information from the models produced. Unfortunately, some of the most powerful inductive learning algorithms generate “black boxes”—that is, the representation of the model makes it virtually impossible to gain any insight into what has been learned. This paper presents a
18#
發(fā)表于 2025-3-24 16:50:19 | 只看該作者
19#
發(fā)表于 2025-3-24 20:13:24 | 只看該作者
20#
發(fā)表于 2025-3-25 00:10:19 | 只看該作者
Conference proceedings 2003tted papers, 40 were accepted for publication in the ECML2003proceedings,and40wereacceptedforpublicationinthePKDD2003 proceedings. All the submitted papers were reviewed by three referees. In ad- tion to submitted papers, the conference program consisted of four invited talks, four tutorials, seven
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