Titlebook: Machine Learning and Data Mining in Pattern Recognition; 8th International Co Petra Perner Conference proceedings 2012 Springer-Verlag Berl

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How Many Trees in a Random Forest?ss a huge computational environment is available. In addition, it was found an experimental relationship for the AUC gain when doubling the number of trees in any forest. Furthermore, as the number of trees grows, the full set of attributes tend to be used within a Random Forest, which may not be in

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A New Learning Structure Heuristic of Bayesian Networks from Datatic designed to reduce the algorithmic complexity without engendering any loss of information. Ultimately, our conceived approach will be tested on a car diagnosis as well as on a Lymphography diagnosis data-bases, while our achieved results would be discussed, along with an exposition of our conduc

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Transductive Relational Classification in the Co-training Paradigmenerated views allow us to overcome the independence problem that negatively affect the performance of co-training methods. Our experimental evaluation empirically shows that co-training is beneficial in the transductive learning setting when mining multi-relational data and that our approach works well with only a small amount of labeled data.

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A New Approach for Association Rule Mining and Bi-clustering Using Formal Concept Analysisy, allowing parallel processing of the tree branches. Experiments conducted to assess its applicability to very large datasets show that FIST memory requirements and execution times are in most cases equivalent to frequent closed itemsets based algorithms and lower than frequent itemsets based algorithms.

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Constructing Target Concept in Multiple Instance Learning Using Maximum Partial Entropyobability of training data, but focus only on the selected subspace. Experimental evaluation explores the effectiveness of using maximum partial entropy in evaluating the merits between the positive and negative bags in the learning.

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0302-9743 issions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and web mining.978-3-642-31536-7978-3-642-31537-4Series ISSN 0302-9743 Series E-ISSN 1611-3349

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