Search results
Results from the WOW.Com Content Network
Self-managed action learning is a variant of Action Learning that dispenses with the need for a facilitator of the action learning set, including in virtual and hybrid settings. [25] [26] [27] There are a number of problems, however, with purely self-managed teams (i.e., with no coach).
Given a training set consisting of examples = (,, ′), where , ′ are observations of a world state from two consecutive time steps , ′ and is an action instance observed in time step , the goal of action model learning in general is to construct an action model , , where is a description of domain dynamics in action description formalism like STRIPS, ADL or PDDL and is a probability ...
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
Active learning: Instead of assuming that all of the training examples are given at the start, active learning algorithms interactively collect new examples, typically by making queries to a human user. Often, the queries are based on unlabeled data, which is a scenario that combines semi-supervised learning with active learning.
Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source), to label new data points with the desired outputs. The human user must possess knowledge/expertise in the problem domain, including the ability to consult/research authoritative sources ...
The “spacing effect” refers to a phenomenon whereby learning, or the creation of a memory, occurs more effectively when information, or exposure to a stimulus, is spaced out.
High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce ...
Upgrade to a faster, more secure version of a supported browser. It's free and it only takes a few moments: