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Knowledge Transfer between Computer Vision and Text Mining
Similarity-based Learning Approaches
Engleski
249 b
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This ground-breaking text/reference diverges§from the traditional view that computer vision (for image analysis) and string§processing (for text mining) are separate and unrelated fields of study,§propounding that images and text can be treated in a similar manner for the§purposes of information retrieval, extraction and classification. Highlighting§the benefits of knowledge transfer between the two disciplines, the text§presents a range of novel similarity-based learning (SBL) techniques founded on§this approach. Topics and features: describes a variety of SBL approaches,§including nearest neighbor models, local learning, kernel methods, and§clustering algorithms; presents a nearest neighbor model based on a novel§dissimilarity for images; discusses a novel kernel for (visual) word§histograms, as well as several kernels based on a pyramid representation; introduces§an approach based on string kernels for native language identification; contains§links for downloading relevant open source code.
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