2007 IEEE Symposium on Computational Intelligence and Data Mining最新文献_第9页

A Novel Complex-Valued Counterpropagation Network 一种新的复值反传播网络

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368856

P. Kalra, D. Mishra, Kanishka Tyagi

{"title":"A Novel Complex-Valued Counterpropagation Network","authors":"P. Kalra, D. Mishra, Kanishka Tyagi","doi":"10.1109/CIDM.2007.368856","DOIUrl":"https://doi.org/10.1109/CIDM.2007.368856","url":null,"abstract":"The counterpropagation network is a combination of competitive network (Kohonen layer) and Grossberg outstar structure. In this paper we have proposed a complex valued representation on conventional forward only counterpropagation network. Many researchers have investigated the computational capabilities of neuron models for real values only. The novel part of the paper is, while considering the complex values equal weightage is given to both the real and imaginary parts. A vectored approach is taken to compute the complex numbers while implementing it with complex valued counterpropagation network (CVCPN). The proposed network is tested on benchmark problem (two spiral problem), Julia's set, rotational transformations and color image compression. The complex valued counterpropagation network (CVCPN) exhibits less percentage of misclassification and error rate is considerably smaller when compared to the equivalent model in backpropagation network. The learning of intermediate forms of vector classes, manipulation with complex numbers, criterion for winning neuron, and the results of the proposed network with various benchmark and classification problems are discussed","PeriodicalId":423707,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence and Data Mining","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117143211","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

More Efficient Classification of Web Content Using Graph Sampling 使用图采样的更有效的Web内容分类

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368914

C. Bennett

{"title":"More Efficient Classification of Web Content Using Graph Sampling","authors":"C. Bennett","doi":"10.1109/CIDM.2007.368914","DOIUrl":"https://doi.org/10.1109/CIDM.2007.368914","url":null,"abstract":"In mining information from very large graphs, processing time as well as system memory become computational bottlenecks as the properties of large graphs must be compared through each iteration of an algorithm. This is a particularly pronounced problem for complex properties. For example, distance metrics are used in many fundamental data mining algorithms including k-nearest neighbors for the classification task. Even the relatively efficient distance and similarity heuristics for large inputs, though, often require processing and memory well beyond linear with respect to the size of the input, and this rapidly becomes intractable with very large inputs. Complex properties such as the distance between two graphs can be extremely costly, but using samples of these large graphs to calculate the same properties proves to reduce memory requirements and processing time significantly without sacrificing quality of classification. Because the vast amount of Web data is easily and robustly represented with graphs, a data reduction technique that preserves the accuracy of mining algorithms on such inputs is important. The sampling techniques presented here show that very large graphs of Web content can be condensed into significantly smaller yet equally expressive graphs that lead to accurate but more efficient classification of Web content","PeriodicalId":423707,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence and Data Mining","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121514608","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: 3

Exploiting Semantic Descriptions of Products and User Profiles for Recommender Systems 为推荐系统开发产品和用户档案的语义描述

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368870

Pingfeng Liu, G. Nie, Donglin Chen

{"title":"Exploiting Semantic Descriptions of Products and User Profiles for Recommender Systems","authors":"Pingfeng Liu, G. Nie, Donglin Chen","doi":"10.1109/CIDM.2007.368870","DOIUrl":"https://doi.org/10.1109/CIDM.2007.368870","url":null,"abstract":"To enable semantics based recommender systems, products and user profiles need to be represented in knowledge uniformly where ontology can be exploited. Product ontology describes the attributes of the product such as appearance, structure, behavior and function, and has a property \"service\" which describes the services related to the product supplied by the products provider. So service ontology need to be constructed due to its great influences on users when they browse and purchase products. User profile is modeled as a set of triple <goal, constraint, preference> where goal is the product a user searches for, constraint indicates the conditions a user prescribes that must be satisfied by the attributes of the goals and preference indicates users' preferences in specific dimensions of the attributes of the goals. The constraint and preference in product attributes are obtained through mining user's past browsing behaviors and transaction records. The mining algorithm is given in this paper. The method of implicit rating and weight evaluation of product attributes are also explored in this paper. A hybrid approach combining semantic similarity with collaborative filtering is proposed to generate the recommendation lists for users where the semantic similarity algorithm is adopted to get the nearest neighbors of the active user. The experiment results are presented which demonstrate that our approach is feasible.","PeriodicalId":423707,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence and Data Mining","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123912792","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: 18

Evaluating Protein Motif Significance Measures: A Case Study on Prosite Patterns 评价蛋白质基序的重要性:以Prosite模式为例

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368869

P. Ferreira, P. Azevedo

引用次数: 9

Influence of a priori Knowledge on Medical Document Categorization 先验知识对医学文献分类的影响

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368868

Lukasz Itert, Wlodzislaw Duch, J. Pestian

引用次数: 7

A Classifier Capable of Handling New Attributes 一个能够处理新属性的分类器

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368891

Dong-Hun Seo, Chi-Hwa Song, W. Lee

引用次数: 0

K2GA: Heuristically Guided Evolution of Bayesian Network Structures from Data K2GA:基于数据的贝叶斯网络结构的启发式引导进化

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368847

E. Faulkner

引用次数: 16

Prediction of Abnormal Behaviors for Intelligent Video Surveillance Systems 智能视频监控系统异常行为预测

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368897

D. Duque, H. Santos, P. Cortez

引用次数: 86

Noise Reduction Approach for Decision Tree Construction: A Case Study of Knowledge Discovery on Climate and Air Pollution 决策树构建中的降噪方法:以气候与空气污染知识发现为例

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368944

K. Fukuda

{"title":"Noise Reduction Approach for Decision Tree Construction: A Case Study of Knowledge Discovery on Climate and Air Pollution","authors":"K. Fukuda","doi":"10.1109/CIDM.2007.368944","DOIUrl":"https://doi.org/10.1109/CIDM.2007.368944","url":null,"abstract":"Data mining is more effective on noisy time series with appropriate data pre-processing. Singular spectrum analysis (SSA) is explored as the noise reduction approach for a decision tree classifier for noisy data. SSA provides groups of additive components, from low to high frequency, by decomposing the noisy time series. In this study, the noisy climate data is decomposed by SSA and is used to construct decision trees to predict the carbon monoxide (CO) air pollution levels. Analysis shows that separating out seasons from the annual data helps the algorithm; the classification accuracy improvements vary by season, with the maximum improvement (from 60.7% to 77.3%) found in summer by removing 6.42% of the high frequency signals, while autumn showed no improvement. Examining decision tree structures provides threshold climate values that impact on different CO levels, e.g., a light wind speed of les 2.5 m/s and any level of temperature inversion formation is found to associate with the high CO level (> 0.70 mg/m3). Overall, data pre-processing using SSA is encouraging to improve the results of any time series data mining approach. Examining decision trees of the climate and air pollution helps increase knowledge about the data, and the studied approaches can be adaptable for various future environmental studies","PeriodicalId":423707,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence and Data Mining","volume":"30 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132722447","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: 9

Link Analysis of Incomplete Relationship Networks 不完全关系网络的链接分析

2007 IEEE Symposium on Computational Intelligence and Data Mining Pub Date : 2007-06-04 DOI: 10.1109/CIDM.2007.368844

E. Harrington

引用次数: 0