All listings for this product
Best-selling in Textbooks
Save on Textbooks
- AU $37.99Trending at AU $73.29
- AU $68.00Trending at AU $71.81
- AU $68.00Trending at AU $70.75
- AU $100.89Trending at AU $103.44
- AU $68.00Trending at AU $80.95
- AU $53.09Trending at AU $58.60
- AU $99.99Trending at AU $112.55
About this product
- DescriptionRegression and classification methods based on similarity of the input to stored examples have t been widely used in applications involving very large sets of high-dimensional data. Recent advances in computational geometry and machine learning, however, may alleviate the problems in using these methods on large data sets. This volume presents theoretical and practical discussions of nearest-neighbor (NN) methods in machine learning and examines computer vision as an application domain in which the benefit of these advanced methods is often dramatic. It brings together contributions from researchers in theory of computation, machine learning, and computer vision with the goals of bridging the gaps between disciplines and presenting state-of-the-art methods for emerging applications.The contributors focus on the importance of designing algorithms for NN search, and for the related classification, regression, and retrieval tasks, that remain efficient even as the number of points or the dimensionality of the data grows very large. The book begins with two theoretical chapters on computational geometry and then explores ways to make the NN approach practicable in machine learning applications where the dimensionality of the data and the size of the data sets make the naive methods for NN search prohibitively expensive. The final chapters describe successful applications of an NN algorithm, locality-sensitive hashing (LSH), to vision tasks.
- Author BiographyGregory Shakhnarovich is a Postdoctoral Research Associate in the Computer Science Department at Brown University
- PublisherMIT Press Ltd
- Date of Publication02/05/2006
- SubjectComputing: Professional & Programming
- Series TitleNeural Information Processing Series
- Place of PublicationCambridge, Mass.
- Country of PublicationUnited States
- ImprintMIT Press
- Content Noteillustrations
- Weight794 g
- Width152 mm
- Height254 mm
- Spine19 mm
- Edited byGregory Shakhnarovich,Piotr Indyk,Trevor Darrell
- Interest AgeFrom 18
This item doesn't belong on this page.
Thanks, we'll look into this.