Detection tasks yield binary responses (yes/no).
Such paradigms have two main advantages over the more conventional tasks with binary decisions.
In the present and many other tasks with binary reward outcome, if the reward probability is P, the action value, or the expected reward, is P and the risk, or the variance of reward, is P(1− P) (Bishop, 2006; Preuschoff et al., 2006, 2008; Rushworth & Behrens, 2008; Schultz et al., 2008).
Furthermore, regardless of whether the subject was matching a face according to gender, identity, or orientation, the format of the task – a binary left/right choice – was held constant across tasks.
We show that it outperforms the previous character embedding for the task of binary tweet sentiment classification, i.e. determining if tweets convey positive or negative sentiment.
Our approach is quite different with this work in which we train classifiers with a sampling strategy and a smooth hinge loss function and parallelize the training task of binary classifiers on several multi-core computers.
For the rule-based category learning task, four binary dimensions were used to generate the stimuli: background color (turquoise or purple), inside color (black or white), inside shape (square or circle), and inside size (big or small).
Figure 3 shows that if one classification task is binary, counter voting turns into majority voting.
Given a protein pair, PPI extraction is a task of binary classification; for example, 〈IL-8, CXCR1〉 is a positive example, and 〈RBP, TTR〉 is a negative example.
The first set of 20 fragments ('Main' fragments) were each highly informative for Main task, defined as the binary classification task of distinguishing class X from class Y.
