Sentence examples for task prediction from inspiring English sources

The data sets used in this work can be divided into four groups by predictive task: prediction of protein stability change on mutation, prediction of protein protein and protein DNA affinity change on mutation.

In this section, we test our support for one sense-making task, prediction, by evaluating the predict missing links algorithm.

The fourth column of each task prediction contains the confidence score (Conf).

In the initial single-task prediction context, a task is defined as the learning of a personality trait.

The target values y of the tasks were calculated using the standard multi-task prediction function (6), which means that the target values do not contain label noise.

In order to concentrate on individual features rather than a group of features, and test the effectiveness of their combinations, we employ feature selection (MRMR) and extraction (PCA) for a single-task prediction.

We use feature analysis and multi-task learning methods in conjunction with the non-verbal features and crowd-sourced annotations from the Video bLOG (VLOG) corpus to perform a multi-domain and multi-task prediction of personality traits.

In addition, in contrast to most of the existing methods, which applied instance selection to classification tasks (discrete prediction), the proposed approach is used to obtain instance selection methods for regression tasks (prediction of continuous values).

Third, to test whether predicting subjects' responses benefits from assuming that there is a task-independent component of their mental representation, we predicted responses using a Gaussian process (GP) classifier that is a state-of-the-art learning algorithm that has no notion of subjective distributions and is optimized directly for within-task prediction.

Given the unpredictability of reward distribution and the complex cognitive processes involved in the task, the prediction and prediction error may change dynamically across the experiment.

Furthermore, the subjective distributions we extracted from the familiarity task also provided across-task predictions in the OOO task that were as accurate as within-task predictions in that task (p = 0.84).