Sentence examples for prior features from inspiring English sources

Much has been made of the difference between "Computer Chess" and Bujalski's three prior features — all of which are contemporary, not historical — but I was struck by the continuities.

The input features to the CRF are related to the lattice information, contextual posterior features, and unigram prior features.

Within the empirical Bayes approach, however, it is normal to adjust the prior features (in our case, the image model parameters) depending on the amount and type of degradation in the observation.c.

To compare the performance of our model in recovering positive hits in RNAi screens with the canonical supervised learning model, we trained a Random Forest model with the same prior features and training sets used in our model.

The performance of a canonical supervised machine-learning model, trained on the same prior features and training space we used for our function-specific classifier model and differing in that it aggregates training data for all GO terms, is shown for comparison (Figure 5).

This indicates that prior features (either features relating to the services or to external factors such as the communities served) may have determined their performance.

The movie is directed by Jon Watts, whose prior feature is "Cop Car," a thriller about kids commandeering a police cruiser — i.e., toying with powers beyond their grasp.

In its depiction of these differences in black American culture and identity, "She's Gotta Have It" is of a piece with and a large-scale extension of Lee's three prior feature films, "Red Hook Summer," "Da Sweet Blood of Jesus," and "Chi-Raq," which appear to be something of an accidental trilogy.

However, not all machine learning methods require a prior feature selection procedure.

This enabled them to train a deep convolutional neural network for text classification tasks involving high-level concepts from scratch with no prior feature engineering or extraction.

It does not need prior feature selection or parameter tuning but preserves the most appealing feature of Bayesian and Decision Tress the ability to handle a large number of descriptors simultaneously.