Titlebook: Databases Theory and Applications; 28th Australasian Da Zi Huang,Xiaokui Xiao,Xin Cao Conference proceedings 2017 Springer International Pu

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Efficient Supervised Hashing via Exploring Local and Inner Data Structure similarity by leveraging pair-wise supervised knowledge. Besides, we integrate discrete constraint to significantly eliminate accumulated errors in learning reliable hash codes and hash functions. We devise an alternative algorithm to efficiently solve the optimization problem. Extensive experiment

OMIT

Learning Robust Graph Hashing for Efficient Similarity Searchn and hashing learning into a unified learning framework. The learning process ensures the optimal graph to be constructed for subsequent hashing learning, and simultaneously the hashing codes can well preserve similarities of data samples. An effective optimization method is devised to iteratively

暴发户

A New Data Mining Scheme for Analysis of Big Brain Signal Datas (e.g. mean, standard deviation) are computed from the extracted pattern. Then aggregating all of the features extracted from each of the patterns in a subject, a feature vector set is created that is fed into random forest (RF) and random tree (RT) classification model, individually for classifyin