In this dissertation we introduce several novel techniques for performing data mining on web documents which utilize graph representations of document content. Graphs are more robust than typical vector representations as they can model structural information that is usually lost when converting the original web document content to a vector representation. For example, we can capture information such as the location, order and proximity of term occurrence, which is discarded under the standard document vector representation models. Many machine learning methods rely on distance computations, centroid calculations, and other numerical techniques. Thus many of these methods have not been applied to data represented by graphs since no suitable graph-theoretical concepts were previously available. We introduce the novel Graph Hierarchy Construction Algorithm (GHCA), which performs topic-oriented hierarchical clustering of web search results modeled using graphs. The system we created around this new algorithm and its prior version is compared with similar web search clustering systems to gauge its usefulness. An important advantage of this approach over conventional web search systems is that the results are better organized and more easily browsed by users. Next we present extensions to classical machine learning algorithms, such as the k-means clustering algorithm and the k-Nearest Neighbors classification algorithm, which allows the use of graphs as fundamental data items instead of vectors. We perform experiments comparing the performance of the new graph-based methods to the traditional vector-based methods for three web document collections. Our experimental results show an improvement for the graph approaches over the vector approaches for both clustering and classification of web documents. An important advantage of the graph representations we propose is that they allow the computation of graph similarity in polynomial time; usually the determination of graph similarity with the techniques we use is an NP-Complete problem. In fact, there are some cases where the execution time of the graph-oriented approach was faster than the vector approaches.
{"title":"Graph-Theoretic Techniques for Web Content Mining","authors":"A. Schenker, A. Kandel, H. Bunke, Mark Last","doi":"10.1142/5832","DOIUrl":"https://doi.org/10.1142/5832","url":null,"abstract":"In this dissertation we introduce several novel techniques for performing data mining on web documents which utilize graph representations of document content. Graphs are more robust than typical vector representations as they can model structural information that is usually lost when converting the original web document content to a vector representation. For example, we can capture information such as the location, order and proximity of term occurrence, which is discarded under the standard document vector representation models. Many machine learning methods rely on distance computations, centroid calculations, and other numerical techniques. Thus many of these methods have not been applied to data represented by graphs since no suitable graph-theoretical concepts were previously available. \u0000We introduce the novel Graph Hierarchy Construction Algorithm (GHCA), which performs topic-oriented hierarchical clustering of web search results modeled using graphs. The system we created around this new algorithm and its prior version is compared with similar web search clustering systems to gauge its usefulness. An important advantage of this approach over conventional web search systems is that the results are better organized and more easily browsed by users. \u0000Next we present extensions to classical machine learning algorithms, such as the k-means clustering algorithm and the k-Nearest Neighbors classification algorithm, which allows the use of graphs as fundamental data items instead of vectors. We perform experiments comparing the performance of the new graph-based methods to the traditional vector-based methods for three web document collections. Our experimental results show an improvement for the graph approaches over the vector approaches for both clustering and classification of web documents. An important advantage of the graph representations we propose is that they allow the computation of graph similarity in polynomial time; usually the determination of graph similarity with the techniques we use is an NP-Complete problem. In fact, there are some cases where the execution time of the graph-oriented approach was faster than the vector approaches.","PeriodicalId":440867,"journal":{"name":"Series in Machine Perception and Artificial Intelligence","volume":"244 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131223987","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}
Introduction to Data Mining Decision Trees Clustering Techniques Ensemble Methods Decomposition Methodology in Data Mining Feature Set Decomposition Space Decomposition Sample Decomposition Function Decomposition Concept Decomposition Automatic Decomposition Conclusions, Advanced Issues and Open Questions.
{"title":"Decomposition Methodology for Knowledge Discovery and Data Mining - Theory and Applications","authors":"O. Maimon, L. Rokach","doi":"10.1142/5686","DOIUrl":"https://doi.org/10.1142/5686","url":null,"abstract":"Introduction to Data Mining Decision Trees Clustering Techniques Ensemble Methods Decomposition Methodology in Data Mining Feature Set Decomposition Space Decomposition Sample Decomposition Function Decomposition Concept Decomposition Automatic Decomposition Conclusions, Advanced Issues and Open Questions.","PeriodicalId":440867,"journal":{"name":"Series in Machine Perception and Artificial Intelligence","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129697307","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}
The robust range image registration using genetic algorithms and the surface interpenetration measure that we provide for you will be ultimate to give preference. This reading book is your chosen book to accompany you when in your free time, in your lonely. This kind of book can help you to heal the lonely and get or add the inspirations to be more inoperative. Yeah, book as the widow of the world can be very inspiring manners. As here, this book is also created by an inspiring author that can make influences of you to do more.
{"title":"Robust Range Image Registration Using Genetic Algorithms and the Surface Interpenetration Measure","authors":"Luciano Silva, O. Bellon, K. Boyer","doi":"10.1142/5714","DOIUrl":"https://doi.org/10.1142/5714","url":null,"abstract":"The robust range image registration using genetic algorithms and the surface interpenetration measure that we provide for you will be ultimate to give preference. This reading book is your chosen book to accompany you when in your free time, in your lonely. This kind of book can help you to heal the lonely and get or add the inspirations to be more inoperative. Yeah, book as the widow of the world can be very inspiring manners. As here, this book is also created by an inspiring author that can make influences of you to do more.","PeriodicalId":440867,"journal":{"name":"Series in Machine Perception and Artificial Intelligence","volume":"21 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116580668","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}
Fuzzy Neural Networks for Storing and Classifying Feedback Fuzzy Associative Memory Regular Fuzzy Neural Networks Polygonal Fuzzy Neural Networks Approximation Analysis of Fuzzy Systems Stochastic Fuzzy Systems and Approximation Application of Fuzzy Neural Networks to Image Restoration
{"title":"Fuzzy Neural Network Theory and Application","authors":"Puyin Liu, Hongxing Li","doi":"10.1142/5493","DOIUrl":"https://doi.org/10.1142/5493","url":null,"abstract":"Fuzzy Neural Networks for Storing and Classifying Feedback Fuzzy Associative Memory Regular Fuzzy Neural Networks Polygonal Fuzzy Neural Networks Approximation Analysis of Fuzzy Systems Stochastic Fuzzy Systems and Approximation Application of Fuzzy Neural Networks to Image Restoration","PeriodicalId":440867,"journal":{"name":"Series in Machine Perception and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129105665","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}
Introduction to Robotics Motion of Rigid Body Mechanical System of Robot Electromechanical System of Robot Control System of Robot Information System of Robot Visual Sensory System of Robot Visual Perception System of Robot Decision-Making System of Robot.
{"title":"Fundamentals of Robotics - Linking Perception to Action","authors":"M. Xie","doi":"10.1142/5230","DOIUrl":"https://doi.org/10.1142/5230","url":null,"abstract":"Introduction to Robotics Motion of Rigid Body Mechanical System of Robot Electromechanical System of Robot Control System of Robot Information System of Robot Visual Sensory System of Robot Visual Perception System of Robot Decision-Making System of Robot.","PeriodicalId":440867,"journal":{"name":"Series in Machine Perception and Artificial Intelligence","volume":"291 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114041252","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}
Introduction to Syntactic Pattern Recognition Introduction to Formal Languages and Automata Error-Correcting Finite-State Automaton for Recognition of Ricker Wavelets Attributed Grammar and Error-Correcting Earley's Parsing Attributed Grammar and Match Primitive Measure (MPM) for Recognition of Seismic Wavelets String Distance and Likelihood Ratio Test for Detection of Candidate Bright Spot Tree Grammar and Automaton for Seismic Pattern Recognition A Hierarchical Recognition System of Seismic Patterns and Future Study.
{"title":"Syntactic Pattern Recognition for Seismic Oil Exploration","authors":"Kou-Yuan Huang","doi":"10.1142/4682","DOIUrl":"https://doi.org/10.1142/4682","url":null,"abstract":"Introduction to Syntactic Pattern Recognition Introduction to Formal Languages and Automata Error-Correcting Finite-State Automaton for Recognition of Ricker Wavelets Attributed Grammar and Error-Correcting Earley's Parsing Attributed Grammar and Match Primitive Measure (MPM) for Recognition of Seismic Wavelets String Distance and Likelihood Ratio Test for Detection of Candidate Bright Spot Tree Grammar and Automaton for Seismic Pattern Recognition A Hierarchical Recognition System of Seismic Patterns and Future Study.","PeriodicalId":440867,"journal":{"name":"Series in Machine Perception and Artificial Intelligence","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128526313","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}
Image processing methodology: images and image processing shape analysis feature detection and object location texture three-dimensional processing pattern recognition. Application to food production: inspection and inspection procedures inspection of baked products cereal grain inspection X-ray inspection image processing in agriculture vision for fish and meat processing system design considerations food processing for the millennium.
{"title":"Image Processing for the Food Industry","authors":"E. R. Davies","doi":"10.1142/4182","DOIUrl":"https://doi.org/10.1142/4182","url":null,"abstract":"Image processing methodology: images and image processing shape analysis feature detection and object location texture three-dimensional processing pattern recognition. Application to food production: inspection and inspection procedures inspection of baked products cereal grain inspection X-ray inspection image processing in agriculture vision for fish and meat processing system design considerations food processing for the millennium.","PeriodicalId":440867,"journal":{"name":"Series in Machine Perception and Artificial Intelligence","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125260892","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}
This 2nd edition is an update of the book "Wavelet Theory and Its Application to Pattern Recognition" published in 2000. Three new chapters, which are research results conducted during 2001-2008, will be added. The book consists of two parts - the first contains the basic theory of wavelet analysis and the second includes applications of wavelet theory to pattern recognition. The new book provides a bibliography of 170 references including the current state-of-the-art theory and applications of wavelet analysis to pattern recognition. Continuous Wavelet Transforms Multiresolution Analysis and Wavelet Bases Some Typical Wavelet Bases Step-Edge Detection by Wavelet Transform Characterization of Dirac-Edges with Quadratic Spline Wavelet Transform Construction of New Wavelet Function and Application to Curve Analysis Skeletonization of Ribbon-like Shapes with New Wavelet Function Feature Extraction by Wavelet Sub-Patterns and Divider Dimensions Document Analysis by Reference Line Detection with 2-D Wavelet Transform Chinese Character Processing with B-Spline Wavelet Transform Classifier Design Based on Orthogonal Wavelet Series
{"title":"Wavelet Theory and Its Application to Pattern Recognition","authors":"Y. Tang, Jiming Liu, Lihua Yang, Hong Ma","doi":"10.1142/4053","DOIUrl":"https://doi.org/10.1142/4053","url":null,"abstract":"This 2nd edition is an update of the book \"Wavelet Theory and Its Application to Pattern Recognition\" published in 2000. Three new chapters, which are research results conducted during 2001-2008, will be added. The book consists of two parts - the first contains the basic theory of wavelet analysis and the second includes applications of wavelet theory to pattern recognition. The new book provides a bibliography of 170 references including the current state-of-the-art theory and applications of wavelet analysis to pattern recognition. Continuous Wavelet Transforms Multiresolution Analysis and Wavelet Bases Some Typical Wavelet Bases Step-Edge Detection by Wavelet Transform Characterization of Dirac-Edges with Quadratic Spline Wavelet Transform Construction of New Wavelet Function and Application to Curve Analysis Skeletonization of Ribbon-like Shapes with New Wavelet Function Feature Extraction by Wavelet Sub-Patterns and Divider Dimensions Document Analysis by Reference Line Detection with 2-D Wavelet Transform Chinese Character Processing with B-Spline Wavelet Transform Classifier Design Based on Orthogonal Wavelet Series","PeriodicalId":440867,"journal":{"name":"Series in Machine Perception and Artificial Intelligence","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129837271","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}
{"title":"New Approaches to Fuzzy Modeling and Control - Design and Analysis","authors":"M. Margaliot, G. Langholz","doi":"10.1142/4446","DOIUrl":"https://doi.org/10.1142/4446","url":null,"abstract":"Fuzzy Lyapunov synthesis fuzzy Lyapunov synthesis and stability analysis adaptive fuzzy controller design inverse optimality for fuzzy controllers hyperbolic approach to fuzzy modelling fuzzy controllers for the hyperbolic state-space model.","PeriodicalId":440867,"journal":{"name":"Series in Machine Perception and Artificial Intelligence","volume":"287 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116371523","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}
From the Publisher: This book presents a powerful hybrid intelligent system based on fuzzy logic, neural networks, genetic algorithms, and related intelligent techniques. The new compensatory genetic fuzzy neural networks have been widely used in fuzzy control, nonlinear system modeling, compression of a fuzzy rule base, expansion of a sparse fuzzy rule base, fuzzy knowledge discovery, time series prediction, fuzzy games, and pattern recognition. The proposed soft computing system is effective in performing both linguistic-word-level fuzzy reasoning and numerical-data-level information processing. The book also presents various novel soft computing techniques.
{"title":"Compensatory Genetic Fuzzy Neural Networks and Their Applications","authors":"Yanqing Zhang, A. Kandel","doi":"10.1142/3678","DOIUrl":"https://doi.org/10.1142/3678","url":null,"abstract":"From the Publisher: \u0000This book presents a powerful hybrid intelligent system based on fuzzy logic, neural networks, genetic algorithms, and related intelligent techniques. The new compensatory genetic fuzzy neural networks have been widely used in fuzzy control, nonlinear system modeling, compression of a fuzzy rule base, expansion of a sparse fuzzy rule base, fuzzy knowledge discovery, time series prediction, fuzzy games, and pattern recognition. The proposed soft computing system is effective in performing both linguistic-word-level fuzzy reasoning and numerical-data-level information processing. The book also presents various novel soft computing techniques.","PeriodicalId":440867,"journal":{"name":"Series in Machine Perception and Artificial Intelligence","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124881848","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}
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