Titlebook: Artificial Intelligence: Methods and Applications; 8th Hellenic Confere Aristidis Likas,Konstantinos Blekas,Dimitris Kalle Conference proce

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书目名称Artificial Intelligence: Methods and Applications影响因子(影响力)学科排名




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书目名称Artificial Intelligence: Methods and Applications网络公开度学科排名




书目名称Artificial Intelligence: Methods and Applications被引频次




书目名称Artificial Intelligence: Methods and Applications被引频次学科排名




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书目名称Artificial Intelligence: Methods and Applications年度引用学科排名




书目名称Artificial Intelligence: Methods and Applications读者反馈




书目名称Artificial Intelligence: Methods and Applications读者反馈学科排名

FACET

A novel evolving fuzzy rule-based classifier,ess either from scratch with an empty rule base or from an initially trained fuzzy model. Importantly, pClass not only adopts the open structure concept, where an automatic knowledge building process can be cultivated during the training process, which is well-known as a main pillar to learn from st

figurine

Sequential Sparse Adaptive Possibilistic Clusteringse algorithms is that they involve certain parameters that need to be estimated accurately beforehand and remain fixed during their execution. Recently, a possibilistic clustering scheme has been proposed that allows the adaptation of these parameters and imposes sparsity in the sense that it forces

evanescent

A Rough Information Extraction Technique for the Dendritic Cell Algorithm within Imprecise CircumstaCA depends on the extracted features and their categorization to their specific signal types. These two tasks are performed during the DCA data pre-processing phase and are both based on the use of the Principal Component Analysis (PCA) information extraction technique. However, using PCA presents a

vitreous-humor

CURB

Play Ms. Pac-Man Using an Advanced Reinforcement Learning Agent real time has been already proved, the high dimensionality of state spaces in most game domains can be seen as a significant barrier. This paper studies the popular arcade video game Ms. Pac-Man and outlines an approach to deal with its large dynamical environment. Our motivation is to demonstrate

聚集

Multi-view Regularized Extreme Learning Machine for Human Action Recognition proper regularization terms in the ELM optimization problem. In order to determine both optimized network weights and action representation combination weights, we propose an iterative optimization process. The proposed algorithm has been evaluated by using the state-of-the-art action video represe

BLINK

Classifying Behavioral Attributes Using Conditional Random Fields conditional random field (CRF). The unary terms of the CRF employ spatiotemporal features (i.e., HOG3D, STIP and LBP). The pairwise terms are based on kinematic features such as the velocity and the acceleration of the subject. As an exact solution to the maximization of the posterior probability o

Gene408

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