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2009 Fifth International Conference on Natural Computation最新文献

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Generalization Performance of ERM Algorithm with Geometrically Ergodic Markov Chain Samples 几何遍历马尔可夫链样本的ERM算法的泛化性能
2009 Fifth International Conference on Natural Computation Pub Date : 2009-08-14 DOI: 10.1109/ICNC.2009.184
Jie Xu, Bin Zou, Jianjun Wang
{"title":"Generalization Performance of ERM Algorithm with Geometrically Ergodic Markov Chain Samples","authors":"Jie Xu, Bin Zou, Jianjun Wang","doi":"10.1109/ICNC.2009.184","DOIUrl":"https://doi.org/10.1109/ICNC.2009.184","url":null,"abstract":"The previous works describing the generalization ability of learning algorithms are based on independent and identically distributed (i.i.d.) samples. In this paper we go far beyond this classical framework by studying the learning performance of the Empirical Risk Minimization (ERM) algorithm with Markov chain samples. We obtain the bound on the rate of uniform convergence of the ERM algorithm with geometrically ergodic Markov chain samples, as an application of our main result we establish the bounds on the generalization performance of the ERM algorithm, and show that the ERM algorithm with geometrically ergodic Markov chain samples is consistent. These results obtained in this paper extend the previously known results of i.i.d. observations to the case of Markov dependent samples.","PeriodicalId":235382,"journal":{"name":"2009 Fifth International Conference on Natural Computation","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126608596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
The Application of Adaptive Immune Algorithm for Reactive Power Optimization 自适应免疫算法在无功优化中的应用
2009 Fifth International Conference on Natural Computation Pub Date : 2009-08-14 DOI: 10.1109/ICNC.2009.726
Lin Jikeng, W. Xudong
{"title":"The Application of Adaptive Immune Algorithm for Reactive Power Optimization","authors":"Lin Jikeng, W. Xudong","doi":"10.1109/ICNC.2009.726","DOIUrl":"https://doi.org/10.1109/ICNC.2009.726","url":null,"abstract":"The adaptive immune algorithm (AIA) developed from immune algorithm (IA), owns faster computation speed and better convergence than that of GA and other stochastic type algorithms, due to its characteristic of having two layers optimization. The paper proposes to apply adaptive immune algorithm for reactive power optimization. The coding method for the control variables based on decimal system is introduced in detail. The test results of example systems demonstrate its feasibility and validity.","PeriodicalId":235382,"journal":{"name":"2009 Fifth International Conference on Natural Computation","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126773327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Traffic Prediction with Reservoir Computing for Mobile Networks 基于库计算的移动网络流量预测
2009 Fifth International Conference on Natural Computation Pub Date : 2009-08-14 DOI: 10.1109/ICNC.2009.685
Pengpeng Yu, Wang Jian-min, Peng Xi-yuan
{"title":"Traffic Prediction with Reservoir Computing for Mobile Networks","authors":"Pengpeng Yu, Wang Jian-min, Peng Xi-yuan","doi":"10.1109/ICNC.2009.685","DOIUrl":"https://doi.org/10.1109/ICNC.2009.685","url":null,"abstract":"The accurate traffic model and prediction of mobile network plays an important role in network planning. It is particularly important for the performance analysis of mobile networks. The study in this paper concerns predicting the traffic of mobile network, which is essentially nonlinear, dynamic and affected by immeasurable parameters and variables. The accurate analytical model of the traffic of the mobile network can be hardly obtained. Therefore a predicting method based on history input-output using correlation analysis ideas and Reservoir Computing (RC) is proposed. Correlation analysis is used to select proper input variables of the model. Reservoir Computing is a recent research area, in which a random recurrent topology is constructed, and only the weights of connections in a linear output layer is trained. This make it possible to solve complex tasks using just linear post-processing techniques. The proposed model has been verified on the data from network monitoring system in China Mobile Heilongjiang Co. Ltd.","PeriodicalId":235382,"journal":{"name":"2009 Fifth International Conference on Natural Computation","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115243291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
P300 Feature Extraction Based on Parametric Model and FastICA Algorithm 基于参数化模型和FastICA算法的P300特征提取
2009 Fifth International Conference on Natural Computation Pub Date : 2009-08-14 DOI: 10.1109/ICNC.2009.160
Qiao Xiaoyan, Li Douzhe, Dong Youer
{"title":"P300 Feature Extraction Based on Parametric Model and FastICA Algorithm","authors":"Qiao Xiaoyan, Li Douzhe, Dong Youer","doi":"10.1109/ICNC.2009.160","DOIUrl":"https://doi.org/10.1109/ICNC.2009.160","url":null,"abstract":"A method based on AR model and Fast ICA algorithm for P300 feature extracting is presented. In the study, the visual evoked signal is obtained via the alternate pictures. Then, principal component analysis (PCA) is used for reducing the dimension of EEG signal, independent component analysis (ICA) is used for removing EOG artifact. And AR model is constructed for filtrating the spontaneous EEG. Finally, a coherence average is used to extract P300 in real-time. The results have shown that this method can perform effectively to extract P300 feature independently to any prior information and avoid the subject’s visual fatigue caused by long time visual evoking. It can be applied on online BCI system.","PeriodicalId":235382,"journal":{"name":"2009 Fifth International Conference on Natural Computation","volume":"211 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115253393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Discovery of Mineralization Predication Classification Rules by Using Gene Expression Programming Based on PCA 基于PCA的基因表达编程的矿化预测分类规则发现
2009 Fifth International Conference on Natural Computation Pub Date : 2009-08-14 DOI: 10.1109/ICNC.2009.367
Dongmei Zhang, Yue Huang, Jing Zhi
{"title":"Discovery of Mineralization Predication Classification Rules by Using Gene Expression Programming Based on PCA","authors":"Dongmei Zhang, Yue Huang, Jing Zhi","doi":"10.1109/ICNC.2009.367","DOIUrl":"https://doi.org/10.1109/ICNC.2009.367","url":null,"abstract":"Classification is one of the fundamental tasks in geology field. In this paper, we propose an evolutionary approach for discovering classification rules of mineralization predication from distinct combinations of geochemistry elements by using gene expression programming (GEP). The innovative part of the paper presents integrated/hybrid model-combine GEP evolution modeling with Principal Component Analysis (PCA), which reduce multidimensional data sets. Mineral deposit with tin and copper in Gejiu is chosen as the research area. MAPGIS and MORPAS are used to extract the value of ore-controlled factors by mapping geologic maps into grid cell. Case study illustrates the proposed GEP approach Based on PCA is more efficient and accurate in a large searching space, compared with Decision Tree (C4.5) and Bayesian Networks.","PeriodicalId":235382,"journal":{"name":"2009 Fifth International Conference on Natural Computation","volume":"260 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122759324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
A Self-Adaptive Hybrid Genetic Algorithm for Data Mining Applications 数据挖掘应用中的自适应混合遗传算法
2009 Fifth International Conference on Natural Computation Pub Date : 2009-08-14 DOI: 10.1109/ICNC.2009.132
Chuan-Hua Zhou, An-Shi Xie, Xin-Wei Xu, Bao-Hua Zhou, Zhang Feng
{"title":"A Self-Adaptive Hybrid Genetic Algorithm for Data Mining Applications","authors":"Chuan-Hua Zhou, An-Shi Xie, Xin-Wei Xu, Bao-Hua Zhou, Zhang Feng","doi":"10.1109/ICNC.2009.132","DOIUrl":"https://doi.org/10.1109/ICNC.2009.132","url":null,"abstract":"Data mining involves nontrivial process of extracting knowledge or patterns from large databases. Many searching and optimization methods are used in data mining. In this paper we propose a Self-Adaptive Hybrid GA (SAHGA), where parameters of population size, crossover rate and mutation rate for each individual in each generation are adaptively fixed. Further, the crossover operator and mutation operator are decided dynamically. Finally, the tabu strategy is involved in the process of evolution. The three measures mentioned above help to maintain the diversity of the population and smooth over premature convergence. The effective performance of the algorithm is then shown using standard testbed functions and a set of classification datamining problems with UCI datasets based on Weka Platform.","PeriodicalId":235382,"journal":{"name":"2009 Fifth International Conference on Natural Computation","volume":"182 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114167616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Local Kernel Mapping for Object Recognition 目标识别的局部核映射
2009 Fifth International Conference on Natural Computation Pub Date : 2009-08-14 DOI: 10.1109/ICNC.2009.419
Baochang Zhang, Hong Zheng, Zhongli Wang
{"title":"Local Kernel Mapping for Object Recognition","authors":"Baochang Zhang, Hong Zheng, Zhongli Wang","doi":"10.1109/ICNC.2009.419","DOIUrl":"https://doi.org/10.1109/ICNC.2009.419","url":null,"abstract":"This paper proposes a new method, named Local Kernel Mapping (LKM), for object recognition. LKM is proposed to capture the nonlinear local relationship by using the kernel function. Different from traditional kernel methods for feature extraction, the proposed method does not need to reserve the training samples. To testify the effectiveness of LKM, we apply it on Local Binary Pattern (LBP), and the experiment results on palmprint show that LKM can improve the performance of the LBP method.","PeriodicalId":235382,"journal":{"name":"2009 Fifth International Conference on Natural Computation","volume":"188 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114174396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
News Video Story Segmentation Based on Naïve Bayes Model 基于Naïve贝叶斯模型的新闻视频故事分割
2009 Fifth International Conference on Natural Computation Pub Date : 2009-08-14 DOI: 10.1109/ICNC.2009.712
W. Jianping, Peng Tianqiang, L. Bicheng
{"title":"News Video Story Segmentation Based on Naïve Bayes Model","authors":"W. Jianping, Peng Tianqiang, L. Bicheng","doi":"10.1109/ICNC.2009.712","DOIUrl":"https://doi.org/10.1109/ICNC.2009.712","url":null,"abstract":"Story boundary detection is the foundation of content based news video retrieval. In this paper, Naive Bayes Model, which has been successfully used in multi-modal feature fusion, is implemented in news video story segmentation. Firstly, we get candidate boundaries through shot detection. Secondly, middle-level features such as visual features, audio type, motion and caption, are extracted from shots around these boundaries to generate input attribute set of the model. Thirdly, we use trained Naive Bayes Model to compute posterior probabilities that a candidate boundary is a real story or not, and get the result according to maximum posterior probability rule. Lastly, post-processing is conducted, removing the non-news stories. Experiment results show that this method is effective and achieves satisfactory precision and recall. The new method requires less computation and is applicable to different types of news programs.","PeriodicalId":235382,"journal":{"name":"2009 Fifth International Conference on Natural Computation","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114187585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
RBFCM Based Heuristic Coordinator Algorithm for KDD 基于RBFCM的KDD启发式协调算法
2009 Fifth International Conference on Natural Computation Pub Date : 2009-08-14 DOI: 10.1109/ICNC.2009.18
Zhen Peng, Bingru Yang, Yonghong Xie
{"title":"RBFCM Based Heuristic Coordinator Algorithm for KDD","authors":"Zhen Peng, Bingru Yang, Yonghong Xie","doi":"10.1109/ICNC.2009.18","DOIUrl":"https://doi.org/10.1109/ICNC.2009.18","url":null,"abstract":"Heuristic coordinator, as an import part in KDD systems based on double bases cooperating mechanism, can simulate “creating intent” of cognitive psychology feature and enhance the ability of self-cognition. With the aim to improve the cognitive feature and the performance of heuristic coordinator, the paper proposes one new heuristic coordinator algorithm, which uses rule based fuzzy cognitive map to represent knowledge and to be effective inference in order to get non-association state of knowledge base for directional mining shortage knowledge in massive database. And the experiment demonstrates that the approach effectively reduces the searching space and increases the intelligence of knowledge discovery compared with the directed hyper-graph based method.","PeriodicalId":235382,"journal":{"name":"2009 Fifth International Conference on Natural Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114435568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Fuzzy Context Neural Network Classifier for Land Cover Classification 土地覆盖分类的模糊上下文神经网络分类器
2009 Fifth International Conference on Natural Computation Pub Date : 2009-08-14 DOI: 10.1109/ICNC.2009.379
Hao Gong, Man Zhu, Wei Li
{"title":"A Fuzzy Context Neural Network Classifier for Land Cover Classification","authors":"Hao Gong, Man Zhu, Wei Li","doi":"10.1109/ICNC.2009.379","DOIUrl":"https://doi.org/10.1109/ICNC.2009.379","url":null,"abstract":"Land cover classification based on statistical pattern recognition technique applied to multispectral remote sensor data is one of the most often used methods of information extraction. Among various classification techniques, neural network classifier makes no strong assumptions about the form of the probability distributions and can be adjusted flexibly to the complexity of the system that they are being used to model, therefore considered to be an attractive choice. However, traditional classifiers are often referred to as point or pixel-based classifiers in that they label a pixel on the basis of its spectral properties alone. In this paper, we present a new context-sensitive neural network classifier, which take into account the spatial context information, using fuzzy method and probabilistic label relaxation. The experiment result shows that the new classifier can reduce some isolated mislabeling and improve the accuracy. The spatial coherence of the classes improved.","PeriodicalId":235382,"journal":{"name":"2009 Fifth International Conference on Natural Computation","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121912352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
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