GRAPH-THEORETIC TECHNIQUES FOR WEB CONTENT MINING by Adam Schenker
This book describes exciting new opportunities for utilizing robust graph representations of data with common machine learning algorithms. Graphs can model additional information which is often not present in commonly used data representations, such as vectors. Through the use of graph distance -- a relatively new approach for determining graph similarity -- the authors show how well-known algorithms, such as k-means clustering and k-nearest neighbors classification, can be easily extended to work with graphs instead of vectors. This allows for the utilization of additional information found in graph representations, while at the same time employing well-known, proven algorithms.To demonstrate and investigate these novel techniques, the authors have selected the domain of web content mining, which involves the clustering and classification of web documents based on their textual substance. Several methods of representing web document content by graphs are introduced; an interesting feature of these representations is that they allow for a polynomial time distance computation, something which is typically an NP-complete problem when using graphs. Experimental results are reported for both clustering and classification in three web document collections using a variety of graph representations, distance measures, and algorithm parameters.In addition, this book describes several other related topics, many of which provide excellent starting points for researchers and students interested in exploring this new area of machine learning further. These topics include creating graph-based multiple classifier ensembles through random node selection and visualization of graph-based data using multidimensional scaling.-
INTEGRATION OF SWARM INTELLIGENCE AND ARTIFICIAL NEURAL NETWORK
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FIGHTING TERROR IN CYBERSPACE
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KERNELS FOR STRUCTURED DATA
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DISSIMILARITY REPRESENTATION FOR PATTERN RECOGNITION, THE: FOUNDATIONS AND APPLICATIONS
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DATA MINING WITH DECISION TREES: THEORY AND APPLICATIONS
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RECOGNITION OF WHITEBOARD NOTES: ONLINE, OFFLINE AND COMBINATION
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GRAPH-BASED KEYWORD SPOTTING
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INVARIANTS FOR PATTERN RECOGNITION AND CLASSIFICATION
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HYBRID METAHEURISTICS: RESEARCH AND APPLICATIONS
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HIDDEN MARKOV MODELS: APPLICATIONS IN COMPUTER VISION
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ACTIVE ROBOT VISION: CAMERA HEADS, MODEL BASED NAVIGATION AND REACTIVE CONTROL
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STUDIES IN PATTERN RECOGNITION: A MEMORIAL TO THE LATE PROFESSOR KING-SUN FU
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WAVELET THEORY AND ITS APPLICATION TO PATTERN RECOGNITION
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EXPERIMENTAL ENVIRONMENTS FOR COMPUTER VISION AND IMAGE PROCESSING
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BRIDGING THE GAP BETWEEN GRAPH EDIT DISTANCE AND KERNEL MACHINES
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MODELLING AND PLANNING FOR SENSOR BASED INTELLIGENT ROBOT SYSTEMS
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PARALLEL IMAGE ANALYSIS: THEORY AND APPLICATIONS
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PARALLEL IMAGE ANALYSIS AND PROCESSING
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INTELLIGENT ROBOTS - SENSING, MODELING AND PLANNING
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PERSONALIZATION TECHNIQUES AND RECOMMENDER SYSTEMS
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DOCUMENT ANALYSIS SYSTEMS II
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MULTISPECTRAL IMAGE PROCESSING AND PATTERN RECOGNITION
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THEORY AND APPLICATIONS OF IMAGE ANALYSIS: SELECTED PAPERS FROM THE 7TH SCANDINAVIAN CONFERENCE ON IMAGE ANALYSIS
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NEURAL NETWORKS IN VISION AND PATTERN RECOGNITION
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SYNTACTIC PATTERN RECOGNITION FOR SEISMIC OIL EXPLORATION
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DOCUMENT IMAGE ANALYSIS
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COMPUTATIONAL INTELLIGENCE IN SOFTWARE QUALITY ASSURANCE
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ROBUST RANGE IMAGE REGISTRATION USING GENETIC ALGORITHMS AND THE SURFACE INTERPENETRATION MEASURE
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IMAGE PATTERN RECOGNITION: SYNTHESIS AND ANALYSIS IN BIOMETRICS
| SKU | Unavailable |
| ISBN 13 | 9789812563392 |
| ISBN 10 | 9812563393 |
| Title | GRAPH-THEORETIC TECHNIQUES FOR WEB CONTENT MINING |
| Author | Adam Schenker |
| Series | Series In Machine Perception And Artificial Intelligence |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | World Scientific Publishing Co Pte Ltd |
| Year published | 2005-05-31 |
| Number of pages | 248 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |