Sentence examples for a training template from inspiring English sources

Let x t and y t ' be the frames of a test segment and a training template, respectively, and assume that y t ' is labeled by a GMM m 1 y t '.

Discarding beta-strand pairs whose interleave value equals or exceeds the threshold guarantees that the MRF will have no beta-strand pairs greater than or equal to that threshold, and thus bounds the computational complexity, which is exponential in the maximum interleave value found in a training template.

A discrete-valued, bivariate hidden Markov model was generated using a provided training template based on results from human known large deletions and duplications, with a conservative transition probability of 1 × 10−.

To optimize the taxonomic classification, a training set template file trimmed to a 100-nt region centered on the V6 region was generated from the 8422-sequence RDP training set version 6.

In the test stage, the baseline model is first used to perform decoding search on a test speech utterance to generate a word lattice; the test speech frames are labeled by the GMMs in the same way as in training; template matching and best path search are then performed on the word lattice to generate the rescored sentence hypothesis (Section 3.3).

These filters are given by a combination of several training templates chosen by a designer in an ad hoc manner.

Given a vast search space of training templates, an iterative algorithm is used to synthesize a filter with an optimized performance in terms of several competing criteria.

To further reduce the costs of memory space and computation, we propose template selection methods to generate template representatives based on the criteria of minimum distance (MDTS) and maximum likelihood (MLTS) and we also propose a template compression method to integrate information from training templates to obtain more informative template representatives.

In template selection, the goal is to choose a small subset of templates as the representatives for the full set of training templates.

Our main work focuses on training templates of noise reduction and edge detection CNNs.

However, certain redundancies in the training templates can be reduced to improve computation and storage efficiency.