Titlebook: Intelligent Data Engineering and Automated Learning – IDEAL 2008; 9th International Co Colin Fyfe,Dongsup Kim,Hujun Yin Conference proceedi

威风

书目名称Intelligent Data Engineering and Automated Learning – IDEAL 2008影响因子(影响力)




书目名称Intelligent Data Engineering and Automated Learning – IDEAL 2008影响因子(影响力)学科排名




书目名称Intelligent Data Engineering and Automated Learning – IDEAL 2008网络公开度




书目名称Intelligent Data Engineering and Automated Learning – IDEAL 2008网络公开度学科排名




书目名称Intelligent Data Engineering and Automated Learning – IDEAL 2008被引频次




书目名称Intelligent Data Engineering and Automated Learning – IDEAL 2008被引频次学科排名




书目名称Intelligent Data Engineering and Automated Learning – IDEAL 2008年度引用




书目名称Intelligent Data Engineering and Automated Learning – IDEAL 2008年度引用学科排名




书目名称Intelligent Data Engineering and Automated Learning – IDEAL 2008读者反馈




书目名称Intelligent Data Engineering and Automated Learning – IDEAL 2008读者反馈学科排名

ungainly

Kernel Regression with a Mahalanobis Metric for Short-Term Traffic Flow Forecastinge effectiveness of the learned metrics. Experiments on real data of urban vehicular traffic flows are performed. Comparisons with traditional kernel regression with the Euclidean metric under two criterions show that the new method is more effective for short-term traffic flow forecasting.

Corral

Extracting Auto-Correlation Feature for License Plate Detection Based on AdaBoostground-lighter-character and the other is lighter-background-deeper-character, training a detector cannot convergent. To avoid this problem, two detectors are designed in the proposed method. Experimental results show the superiority of proposed method.

ascend

Intentional

Proposal of Exploitation-Oriented Learning PS-r#r* requires .(..) memories where . and . are the numbers of types of a sensory input and an action.In this paper, we propose PS-r. that can learn an useful rational policy in the POMDPs environments by .(.) memories. We confirm the effectiveness of PS-r. in numerical examples.

CHECK

Nonnegative Matrix Factorization (NMF) Based Supervised Feature Selection and Adaptationive Supervised Feature Adaptation (NSFA) learning algorithm. The supervised feature selection and adaptation improve the classification performance which is fully confirmed by simulations with text-document classification problem. Moreover, the non-negativity constraint, of this algorithm, provides biologically plausible and meaningful feature.

人类的发源

乐章

A New Adaptive Strategy for Pruning and Adding Hidden Neurons during Training Artificial Neural Netwn problems, including Australian credit card assessment, diabetes, heart, iris, and thyroid problems. The experimental results show that the proposed strategy can design compact ANNs with good generalization ability.

indicate

Using Kullback-Leibler Distance in Determining the Classes for the Heart Sound Signal ClassificationKL (Kullback-Leibler) distance between different signal types to determine if they should belong to the same class. From the classification experiments on the heart sound data consisting of 25 different types of signals, the proposed method proved to be quite efficient in determining the optimal set of classes.

有机体