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Can I read Graph-Theoretic Techniques for Web Content Mining (Machine Perception and Artificial Intelligence) (Series in Machine Perception and Artificial Intelligence) on EtoBox?
Graph-Theoretic Techniques for Web Content Mining (Machine Perception and Artificial Intelligence) (Series in Machine Perception and Artificial Intelligence) by Adam Schenker; Abraham Kandel; Horst Bunke; Mark Last is a nonfiction available to read on EtoBox.
What is Graph-Theoretic Techniques for Web Content Mining (Machine Perception and Artificial Intelligence) (Series in Machine Perception and Artificial Intelligence) about?
<p><p>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.</p> <p>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
Who reads Graph-Theoretic Techniques for Web Content Mining (Machine Perception and Artificial Intelligence) (Series in Machine Perception and Artificial Intelligence)?
It is typically read by self-directed learners exploring a subject in depth.
Common subject areas: history, science, philosophy, social sciences.
- Author
- Adam Schenker; Abraham Kandel; Horst Bunke; Mark Last
- Publisher
- World Scientific; World Scientific Publishing Company
- Published
- 2005
- Language
- EN
- ISBN
- 9781281372574
- Category
- nonfiction
- Subjects
- Management, Computer Science, Stem
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