MARC Record from harvard_bibliographic_metadata

LEADER: 02664nam a22004815a 4500
001 014162730-1
005 20141003191223.0
008 140828s2014 gw | s ||0| 0|eng d
020 $a9783319074160
020 $a9783319074160
020 $a9783319074153
024 7 $a10.1007/978-3-319-07416-0$2doi
035 $a(Springer)9783319074160
040 $aSpringer
050 4 $aT385
050 4 $aTA1637-1638
050 4 $aTK7882.P3
072 7 $aUYQV$2bicssc
072 7 $aCOM016000$2bisacsh
082 04 $a006.6$223
100 1 $aHe, Ran,$eauthor.
245 10 $aRobust Recognition via Information Theoretic Learning /$cby Ran He, Baogang Hu, Xiaotong Yuan, Liang Wang.
264 1 $aCham :$bSpringer International Publishing :$bImprint: Springer,$c2014.
300 $aXI, 110 p. 29 illus., 25 illus. in color.$bonline resource.
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
347 $atext file$bPDF$2rda
490 1 $aSpringerBriefs in Computer Science,$x2191-5768
505 0 $aIntroduction -- M-estimators and Half-quadratic Minimization -- Information Measures -- Correntropy and Linear Representation -- ℓ1 Regularized Correntropy -- Correntropy with Nonnegative Constraint.
520 $aThis Springer Brief represents a comprehensive review of information theoretic methods for robust recognition. A variety of information theoretic methods have been proffered in the past decade, in a large variety of computer vision applications; this work brings them together, attempts to impart the theory, optimization and usage of information entropy. The authors resort to a new information theoretic concept, correntropy, as a robust measure and apply it to solve robust face recognition and object recognition problems. For computational efficiency, the brief introduces the additive and multiplicative forms of half-quadratic optimization to efficiently minimize entropy problems and a two-stage sparse presentation framework for large scale recognition problems. It also describes the strengths and deficiencies of different robust measures in solving robust recognition problems.
650 10 $aComputer science.
650 0 $aComputer science.
650 0 $aComputer vision.
650 24 $aComputer Imaging, Vision, Pattern Recognition and Graphics.
650 24 $aImage Processing and Computer Vision.
700 1 $aWang, Liang,$eauthor.
700 1 $aYuan, Xiaotong,$eauthor.
700 1 $aHu, Baogang,$eauthor.
776 08 $iPrinted edition:$z9783319074153
830 0 $aSpringerBriefs in Computer Science.
988 $a20140917
906 $0VEN