Heterogeneous Metric Learning of Categorical Data with Hierarchical Couplings - 2018 PROJECT TITLE :Heterogeneous Metric Learning of Categorical Data with Hierarchical Couplings - 2018ABSTRACT:Learning applicable metric is crucial for effectively capturing complex knowledge characteristics. The metric learning of categorical data with hierarchical coupling relationships and local heterogeneous distributions is very difficult nonetheless rarely explored. This Project proposes a Heterogeneous mEtric Learning with hIerarchical Couplings (HELIC for brief) for this kind of categorical knowledge. HELIC captures each low-level worth-to-attribute and high-level attribute-to-class hierarchical couplings, and divulges the intrinsic heterogeneities embedded in every level of couplings. Theoretical analyses of the effectiveness and generalization error bound verify that HELIC effectively represents the above complexities. Intensive experiments on 30 data sets with numerous characteristics demonstrate that HELIC-enabled classification considerably enhances the accuracy (up to forty.ninety three %), compared with 5 state-of-the-art baselines. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Efficient Parameter Estimation for Information Retrieval Using Black-Box Optimization - 2018 MCS-GPM: Multi-Constrained Simulation Based Graph Pattern Matching in Contextual Social Graphs - 2018
