Titlebook: Agents and Data Mining Interaction; 6th International Wo Longbing Cao,Ana L. C. Bazzan,Philip S. Yu Conference proceedings 2010 The Editor(

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书目名称Agents and Data Mining Interaction影响因子(影响力)




书目名称Agents and Data Mining Interaction影响因子(影响力)学科排名




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书目名称Agents and Data Mining Interaction网络公开度学科排名




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书目名称Agents and Data Mining Interaction被引频次学科排名




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书目名称Agents and Data Mining Interaction年度引用学科排名




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书目名称Agents and Data Mining Interaction读者反馈学科排名

BACLE

ferment

Pilot Study: Agent-Based Exploration of Complex Data in a Hospital Environmentnter on data taken from hospital admission records, tracking patient attributes and how they relate to patient outcomes. The focus of this work is to highlight three design principles: 1) using an iterative process between the modeling of a system and the grounding of that simulation with real-world

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Multi-agent Information Retrieval in Heterogeneous Industrial Automation Environmentsss control systems, computer technology and telecommunication) leads to an increasing complexity of systems and applications. Even though integration has always been one of the major goals of automation, it is still challenging. Currently, users have to run a large number of specific software tools

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金丝雀

Probabilistic Modeling of Mobile Agents’ Trajectoriesctation-maximization algorithm to build a Gaussian mixture model of the spatial density of agents’ movement. On the second level, agents’ trajectories as expressed as sequences of the components of the mixture model; the sequences are subsequently used to train hidden Markov models. The trained hidd

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relieve

Albinism

Clustering in a Multi-Agent Data Mining Environmentts, running several different clustering algorithms, to determine a ”best” cluster configuration. The issue of determining the most appropriate configuration is a challenging one, and is addressed in this paper by considering two metrics, total Within Group Average Distance (WGAD) to determine clust

exostosis

Time-Based Reward Shaping in Real-Time Strategy Gamesconcurrently. This problem cannot be optimally solved through general Q-learning techniques, so we propose a solution using a Semi Markov Decision Process (SMDP). We present a time-based reward shaping technique, TRS, to speed up the learning process in reinforcement learning. Especially, we show th