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

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書目名稱Intelligent Data Engineering and Automated Learning – IDEAL 2008影響因子(影響力)




書目名稱Intelligent Data Engineering and Automated Learning – IDEAL 2008影響因子(影響力)學科排名




書目名稱Intelligent Data Engineering and Automated Learning – IDEAL 2008網(wǎng)絡公開度




書目名稱Intelligent Data Engineering and Automated Learning – IDEAL 2008網(wǎng)絡公開度學科排名




書目名稱Intelligent Data Engineering and Automated Learning – IDEAL 2008被引頻次




書目名稱Intelligent Data Engineering and Automated Learning – IDEAL 2008被引頻次學科排名




書目名稱Intelligent Data Engineering and Automated Learning – IDEAL 2008年度引用




書目名稱Intelligent Data Engineering and Automated Learning – IDEAL 2008年度引用學科排名




書目名稱Intelligent Data Engineering and Automated Learning – IDEAL 2008讀者反饋




書目名稱Intelligent Data Engineering and Automated Learning – IDEAL 2008讀者反饋學科排名

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.

人類的發(fā)源

樂章

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.

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