Through the above method, we generate the candidates state set ( {mathbf{X}}_t=left{{mathbf{x}}_t^1,{mathbf{x}}_t^2,dots, {mathbf{x}}_t^nright} ), where n is the number of candidates sampled at each frame.
The approach was examined by pyrosequencing of cDNA, and 87% of the candidates sampled contained expressed polymorphisms.
Hansen, B. M. S. & Murray, N. Testing in situ assembly with the Kepler planet candidate sample.
To compute this reduction, the expected statistics of unknown data to be sampled by different candidate sampling behaviors are assimilated.
The object convex hull consists of a principle component analysis (PCA) subspace, and the candidate convex hull is constructed by all candidate samples with the sparsity constraint.
With our design, we treat the arrived batches of data as candidate samples and choose samples based on systematic sampling to predict the characteristics of intermediate data.
In our tracker, samples and templates are hashed to binary matrices, and the hamming distance is used to measure confidence of candidate samples.
A set of candidate sampling functions are then prepared from the adaptive PDF, among which the one with maximum energy is selected as the final sampling function.
In this work, we propose a design protocol to generate a more significant candidate sample set for active learning, aiming at reducing the unlabeled sample search complexity, and eventually improving the classification performance.
Based on those calculations and the assumed d-t neutron yield, the activities induced by d-t neutrons are estimated with the inventory code FISPACT-2007 for candidate sample materials: Si, Cu, Al, Fe, Nb, Co, Ti, and Ni.
