Hash-SVM: Scalable kernel machines for large-scale visual classification

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2014, Pages 979-986

  Mu, Yadong ^a   Hua, Gang ^b   Fan, Wei ^a   Chang, Shih Fu ^c  

^a HUAWEI NOAH S ARK LAB   (China)

^b STEVENS INSTITUTE OF TECHNOLOGY   (United States)

^c Och Spine at New York Presbyterian Hospitals   (United States)

Author keywords

Kernel SVM; Locality sensitive hashing; random subspace

Indexed keywords

COST REDUCTION; PATTERN RECOGNITION;

BINARY REPRESENTATIONS; COMPREHENSIVE ANALYSIS; KERNEL APPROXIMATION; KERNEL SVM; LOCALITY SENSITIVE HASHING; RANDOM SUBSPACES; REPRODUCING KERNEL HILBERT SPACES; VISUAL CLASSIFICATION;

SUPPORT VECTOR MACHINES;

EID: 84911369278     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2014.130     Document Type: Conference Paper

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