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.

��ber diesen Titel� kann sich auf eine andere Ausgabe dieses Titels beziehen.

Anbieterinformationen

Hello! Ria Christie Collections is an online venture that was initially set up in 2012 to sell books. We do not have a physical high street store. We are professional online booksellers. We only sell brand new books in perfect condition that we source from various suppliers and the publishers. Primarily, our aim is to provide an excellent service to all our customers. We always work as a team to achieve this. Our other objectives are to: 1. Ensure that all our products reach their destination quickly in a safe and secure manner 2. Answer to all our customer queries within 24 hours 3. Ensure that our customers are happy with their purchases 4. Provide all the items at a competitive price 5. Always listen to our customers Ria Christie Collections is not a registered company. It is a Sole Trader venture. Other key information is shown below: Contact Person Name: Rakesh Luchmun (Mr) Storefront Name: Ria Christie Collections Place of Establishment Address: Suite B; ARUN House; ARUN Building Arundel Road Uxbridge UB8 2RR United Kingdom E-Mail Address: riachristie@hotmail.co.uk VAT Number: GB 160 5650 25 We always work hard and aim to comply with all of Abebooks Policies. If you have any issues, please do not hesitate to write to us whether before or after a purchase. We promise to reply to you promptly and, in any case, within 24 hours. Thank you kindly! Yours sincerely Mr Rakesh Luchmun (Founder) and the Ria Christie Collections Team

Zur Homepage des Verk�ufers

Unternehmensdaten des Verk�ufers

Ryefield Investments Limited
175 Pield Heath Road, Uxbridge, UB8 3NL, United Kingdom

Gesch�ftsbedingungen:

All Returns and Refund are as per Abebooks policies.

Versandinformationen:

Orders usually ship within 2 business days. If your book order is heavy or oversized, we may contact you to let you know extra shipping is required. Thank you!

Zahlungsarten
akzeptiert von diesem Verk�ufer

Zu Kaufgesuchen hinzuf�gen

Verwandte Artikel zu GRAPH-THEORETIC TECHNIQUES FOR WEB CONTENT MINING (Machine...

GRAPH-THEORETIC TECHNIQUES FOR WEB CONTENT MINING (Machine Perception and Artificial Intelligence)

Schenker, Adam; Bunke, Horst; Last, Mark; Kandel, Abraham

Neu - Hardcover

Unternehmensdaten des Verk�ufers