Titlebook: Machine Learning and Data Mining in Pattern Recognition; 7th International Co Petra Perner Conference proceedings 2011 Springer-Verlag GmbH

并排上下

Granular Instances Selection for Fuzzy Modelinge largely studied especially in the classification problem. However, little work has been done to implement instances selection in fuzzy modeling application. In this paper, we present a framework for fuzzy modeling using the granular instances selection. This method is based on the information gran

Frisky

Parameter-Free Anomaly Detection for Categorical Dataions of expected behaviors. It is a major issue of data mining for discovering novel or rare events, actions and phenomena. We investigate outlier detection from a . data set. The problem is especially challenging because of difficulty in defining a meaningful similarity measure for categorical data

Preserve

Fuzzy Semi-supervised Support Vector Machinesining set to learn accurate classifiers. For this, it uses both labelled and unlabelled data for training. It also modulates the effect of the unlabelled data in the learning process. Empirical evaluations showed that by additionally using unlabelled data, FSS-SVM requires less labelled training dat

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冲击力

混合物

consolidate

轻快带来危险

BINGE

Investigation in Transfer Learning: Better Way to Apply Transfer Learning between Agentsthm and combines Case-Based Reasoning (CBR) and Heuristically Accelerated Reinforcement Learning (HARL) techniques..The experiments were made comparing differents approaches of Transfer Learning were actions learned in the acrobot problem can be used to speed up the learning of the policies of stabi

AV-node

Exploration Strategies for Learned Probabilities in Smart Terrainlities that different types of objects meet needs, based on objects it has previously explored. This requires a rational strategy for determining which objects to explore next based on distances to objects, prevalence of similar objects, and amount of information the agent expects to gain. We define