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An American poster from the 1940s. A supervisor, or lead, (also known as foreman, boss, overseer, facilitator, monitor, area coordinator, line-manager or sometimes gaffer) is the job title of a lower-level management position and role that is primarily based on authority over workers or a workplace. [1]
Hence, a supervised learning algorithm can be constructed by applying an optimization algorithm to find . When g {\displaystyle g} is a conditional probability distribution P ( y | x ) {\displaystyle P(y|x)} and the loss function is the negative log likelihood: L ( y , y ^ ) = − log P ( y | x ) {\displaystyle L(y,{\hat {y}})=-\log P(y|x ...
Supervision is the act or function of overseeing something or somebody. It is the process that involves guiding, instructing and correcting someone.
In self-supervised feature learning, features are learned using unlabeled data like unsupervised learning, however input-label pairs are constructed from each data point, enabling learning the structure of the data through supervised methods such as gradient descent. [9] Classical examples include word embeddings and autoencoders.
Capability of employees: if employees are highly capable, need little supervision, and can be left on their own, e.g., Theory Y type of people, they need not be supervised closely as they are motivated and take initiative to work; as such, the span of control may be broader.
It is a type of supervised learning, a method of machine learning where the categories are predefined, and is used to categorize new probabilistic observations into said categories. When there are only two categories the problem is known as statistical binary classification.
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The success rate for part-of-speech tagging algorithms is at present much higher than that for WSD, state-of-the art being around 96% [8] accuracy or better, as compared to less than 75% [citation needed] accuracy in word sense disambiguation with supervised learning. These figures are typical for English, and may be very different from those ...