Finally, the analysis revealed that biologically plausible unbalanced weighting of feature maps (e.g. [ 7]) is crucial for simulating search asymmetries of line orientation, with vertical and horizontal orientation weighted higher than diagonal orientation.
On top of simple user feedback in [21], such as accepting and rejecting suggestions, complex feedback like choosing the best features, suggestions for the re-weighting of features, proposing new features and combining features remarkably improve the system.
Let ( w_1, w_2, w_3 ) be the weight of features Name, Description and Category, respectively.
A user-defined threshold is determined, and weight of features that exceeds this threshold is selected as important features [35].
We compute the classification loss in a feature space with hypothesis-margin and minimize the loss by optimizing the weights of features.
We introduce plenty of easy-computed non-syntactical features to deal with SMT system errors plus the quantum-behaved particle swarm optimization (QPSO) algorithm to adjust the weights of features.
Once the similarity between two groups on each feature is estimated, the similarity between two groups is then estimated by a weighted average aggregation of the similarity between them on all considered features as follows: Let ( w_1, w_2, w_3 ) be the weight of features Name, Description and Category, respectively.
where (mathbf {f}_{i,j}^{a}) and (mathbf {f}_{i,j}^{s}) are feature vectors in (8) and (9) encoding local appearance of the interacting object and global pose of the target person, respectively, and Ω > 0 is an empirically determined diagonal matrix that adjusts the weight of features.
On the other hand, to ensure the sparsity and low redundancy simultaneously, we introduce a novel regularization term into the objective function with the nonnegative constraints imposed, which can be viewed as the combination of the matrix norms ||⋅||m1 and ||⋅||m2 on the weights of features.
