Proceedings oftheSixthinternationalconference on Intelligent System and Knowledge Engineering presents selected papers from the conference ISKE 2011, held December 15-17 in Shanghai, China. this proceedings doesnt on...
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ISBN:
(纸本)9783642256578
Proceedings oftheSixthinternationalconference on Intelligent System and Knowledge Engineering presents selected papers from the conference ISKE 2011, held December 15-17 in Shanghai, China. this proceedings doesnt only examine original research and approaches in the broad areas of intelligent systems and knowledge engineering, but also present new methodologies and practices in intelligent computing paradigms. the book introduces the current scientific and technical advances in the fields of artificial intelligence, machinelearning, patternrecognition, datamining, information retrieval, knowledge-based systems, knowledge representation and reasoning, multi-agent systems, natural-language processing, etc. Furthermore, new computing methodologies are presented, including cloud computing, service computing and pervasive computing with traditional intelligent methods. the proceedings will be beneficial for both researchers and practitioners who want to utilize intelligent methods in their specific research fields. Dr. Yinglin Wang is a professor at the Department of Computer Science and Engineering, Shanghai Jiao Tong University, China; Dr. Tianrui Li is a professor at the School of Information Science and Technology, Southwest Jiaotong University, China.
pattern classification has been successfully applied in many problem domains, such as biometric recognition, document classification or medical diagnosis. Missing or unknown data are a common drawback that pattern rec...
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pattern classification has been successfully applied in many problem domains, such as biometric recognition, document classification or medical diagnosis. Missing or unknown data are a common drawback that patternrecognition techniques need to deal with when solving real-life classification tasks. machinelearning approaches and methods imported from statistical learningtheory have been most intensively studied and used in this subject. the aim of this work is to analyze the missing data problem in pattern classification tasks, and to summarize and compare some of the well-known methods used for handling missing values.
It is estimated that over 8 million cell phones are lost or stolen each year [7];often the loss of a cell phone means the loss of personal data, time and enormous aggravation. In this paper we present machine-learning...
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ISBN:
(纸本)9781424477425
It is estimated that over 8 million cell phones are lost or stolen each year [7];often the loss of a cell phone means the loss of personal data, time and enormous aggravation. In this paper we present machine-learning based algorithms by which a cell phone can discern that it may be lost, and take steps to enhance its chances of being successfully recovered. We use data collected from the Reality mining project [10] to create a suite of realistic test cases that model lost cell phone behavior. On these data sets our best algorithms can identify cases of a lost mobile device, based on its behavior over the previous 3 hours, with close to 100% accuracy. In addition, the algorithm generates false positive identifications with probability less than 3%;for individuals with relatively predictable lifestyles the False Positive Rate is substantially less. We also use the Reality miningdata to construct a set of test cases that model the behavior of a stolen phone, and show that similar algorithmic techniques give reasonable results in this setting as well.
this paper describes the possibilities of using artificial neural networks in the following fields of machinelearning: datamining and semantic integration in large databases. Possibility of using analog components f...
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ISBN:
(纸本)9789662191103
this paper describes the possibilities of using artificial neural networks in the following fields of machinelearning: datamining and semantic integration in large databases. Possibility of using analog components for developing neural networks is investigated.
Opinion mining (OM) and Sentiment Analysis problems lay in the conjunction of such fields as Information Retrieval and Computational Linguistics. As the problems are semantic oriented, the solution must be looked for ...
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ISBN:
(纸本)9783642106866
Opinion mining (OM) and Sentiment Analysis problems lay in the conjunction of such fields as Information Retrieval and Computational Linguistics. As the problems are semantic oriented, the solution must be looked for not in data as such, but in its meaning, considering complex (both internal and external,) domain specific context relations. this paper presents Opinion mining as a specific definition of structural patternrecognition problem. Neuronal Group learning, earlier presented as general structural data analysis tool, is specialised to infer annotations from natural language text.
this paper presents a distributed Support Vector machine (SVM) algorithm in order to detect malicious software (malware) on a network of mobile devices. the light-weight system monitors mobile user activity in a distr...
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Recent years have witnessed the emergence of Smart Environments technology for assisting people withtheir daily routines and for remote health monitoring. A lot of work has been done in the past few years on Activity...
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this paper presents a method of insect recognition using computer vision technology. First, we extracted fourteen features from images of some species of insects. these features are rectangularity, elongation, roundne...
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Unsupervised classification or clustering is an important data analysis technique demanded in various fields including machinelearning, datamining, patternrecognition, image analysis and bioinformatics. Recently a ...
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data Warehousing and datamining are two mature disciplines in their own right. Yet, they have developed largely separate from each other, despite the fact that techniques developed for patternrecognition such as Clu...
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ISBN:
(纸本)9788988678312
data Warehousing and datamining are two mature disciplines in their own right. Yet, they have developed largely separate from each other, despite the fact that techniques developed for patternrecognition such as Clustering and Visualization in the datamining discipline have much to offer in the design of data Warehouses. this is somewhat surprising, given that the two disciplines have broadly the same set of objectives, although the techniques that they employ are admittedly quite different from each other. this may be due to the lack of a suitable methodology for integrating methods such as clustering and pattern visualization into data warehousing design. In this research, we propose such a methodology and report on its application to two case studies involving real world data taken from the UCI machinelearning repository. We demonstrate how data clustering and visualization methods, working in conjunction with each other can be used to gain new insights and build more meaningful dimensions which may not be obvious to human data warehouse designers.
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