Text

Nearest-neighbor methods in learning and vision :theory and practice

---

" ... held in Whistler, British Columbia ... annual conference on Neural Information Processing Systems (NIPS) in December 2003"--Preface.Regression and classification methods based on similarity of the input to stored examples have not 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.OCLC-licensed vendor bibliographic record.

---

Availability

No copy data

Detail Information

Series Title

-

Call Number

-

Publisher

Cambridge, Mass. : : MIT Press,., 2005.

Collation

1 online resource (vi, 252 pages) :illustrations.

Language

English

ISBN/ISSN

9780262256957

Classification

NONE

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

-

Subject(s)

Algorithms
Geometry
Machine Learning
Nearest neighbor analysis (Statistics)

Specific Detail Info

-

Statement of Responsibility

edited by Gregory Shakhnarovich, Trevor Darrell, Piotr Indyk.

Other Information

Cataloger

Kholif Basri

Source

https://doi.org/10.7551/mitpress/4908.001.0001

Validator

Kholif Basri

Digital Object Identifier (DOI)

-

Journal Volume

-

Journal Issue

-

Subtitle

-

Parallel Title

-

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment