Most learning algorithms work most effectively when their training data contain completely specified labeled samples. In many diagnostic tasks, however, the data will include the values of only some of the attributes; we model this as a blocking process that hides the values of those attributes

Abstract. Active learning algorithms attempt to accelerate the learning process by requesting labels for the most informative items first. In real-world problems, however, there may exist unlabeled items that are irrelevant to the user’s classification goals. Queries about these points slow down

The general approach for automatically driving data collection using information from previously acquired data is called active learning. Traditional active learning addresses the problem of choosing the unlabeled examples for which the class labels are queried with the goal of learning a

, a setting previously not being amenable to the pairwise decomposition technique. The key idea of the approach is to introduce an artificial calibration label that, in each example, separates the relevant from the irrelevant labels. We show that this technique can be viewed as a combination

In a neo-classical setting of equity-valuation, this paper develops a principle of dividend policy irrelevancy (DPI) to identify and exploit characteristics of earnings. The latter refers to the idea that a value-relevant variable can not reasonably be labeled "earnings " unless

Multi-label learning methods assign multiple labels to one object. In practice, in addition to differentiating relevant labels from irrelevant ones, it is often desired to rank the relevant labels for an object, whereas the rankings of irrelevant labels are not important. Such a requirement

Abstract. We propose a novel method for removing irrelevant frames from a video given user-provided frame-level labeling for a very small number of frames. We first hypothesize a number of candidate areas which possibly contain the object of interest, and then figure out which area(s) truly contain

models of them. SA claims also that rapid, real-time interaction with a dynamically changing environment is not amenable to symbolic interpretation of the sort espoused by the cognitive science of recent decades. Planning and representation, central to symbolic theories, are claimed to be irrelevant

shadowing. In contrast, listeners with AP suffered substantial interference in shadowing syllables sung with incongruent pitch as well as in naming pitches sung with incongruent syllables. In the experiment of irrelevant sound effect, sequences of randomly-ordered seven musical-pitch syllables (mi, so, la

Most learning algorithms work most e ectively when their training data contain completely speci ed labeled samples. In many diagnostic tasks, however, the data will include the values of only some of the attributes � we model this as a blocking process that hides the values of those attributes from