@article{1385552,
      recid = {1385552},
      author = {Shakhnarovich, Gregory. and Darrell, Trevor. and Indyk,  Piotr.},
      title = {Nearest-neighbor methods in learning and vision : theory  and practice /},
      publisher = {MIT Press,},
      address = {Cambridge, Mass. :},
      pages = {1 online resource (vi, 252 pages) :},
      year = {2005},
      note = {" ... held in Whistler, British Columbia ... annual  conference on Neural Information Processing Systems (NIPS)  in December 2003"--Preface.},
      abstract = {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.},
      url = {http://library.usi.edu/record/1385552},
}