Examples of using Supervised learning in English and their translations into Greek
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Supervised learning(classification• regression).
However, the systems have been built with an older programming approach called"supervised learning.".
Supervised Learning, Generalized Simulations.
For more on collecting andusing labels for supervised learning for text, see Grimmer and Stewart(2013).
Supervised Learning: Fundamentals of function approximation.
Support vector machines(SVMs) are a set of related supervised learning methods used for classification and regression.
Supervised Learning decision trees, support vector machines.
Theoretical benefits in machine learning mainly handle a sort of inductive learning called supervised learning.
For more on supervised learning, see James et al.
Theoretical results in machine learning mainly deal with a type of inductive learning called supervised learning.
A type of supervised learning problem is classification.
Depending on the precise nature of the probability model,naive Bayes classifiers can be trained very efficiently in a supervised learning setting.
Supervised Learning: decision trees, support vector machines.
There's a thin layer we learn through supervised learning, and a little sum we learn through reinforcement learning. .
Supervised learning is the machine learning task of inferring a function from labeled training data.
In machine learning, this approach-using labeled examples to create a model that can then label new data-is called supervised learning.
In supervised learning, an algorithm is given samples that are labeled in some useful way.
Support vector machines(SVMs),also known as support vector networks, are a set of related supervised learning methods used for classification and regression.
This distinguishes unsupervised learning from supervised learning and reinforcement learning. .
In community ecology, the term"classification" normally refers to cluster analysis, i.e.,a type of unsupervised learning, rather than the supervised learning described in this article.
In supervised learning, each example is a pair consisting of an input object(typically a vector) and a desired output value.
This sounds like a massive job, but they solved it using a powerful trick that is common in data science butrelatively rare in social science: supervised learning; see figure 2.5.
A supervised learning algorithm analyses training data and produces an inferred function which can then be used to map new examples.
Other fields may use different terminology: e.g. in community ecology, the term"classification" normally refers to cluster analysis, i.e.,a type of unsupervised learning, rather than the supervised learning described in this article.
Next, in the supervised learning step, Blumenstock built a model to predict the survey response for each person based on their features.
Thus, rather than manually reading and labeling 11 million posts-which would be logistically impossible-King and colleagues manually labeled a smallnumber of posts and then used supervised learning to estimate the sentiment of all the posts.
Next, in the supervised learning step, Blumenstock built a statistical model to predict the survey response for each person based on their features.
Thus, rather than manually reading and labeling 11 million posts-which would be logistically impossible-King and colleagues manually labeled a small number of posts andthen used supervised learning to estimate the sentiment of all the posts.
It differs from standard supervised learning in that correct input/output pairs are never presented, nor imprecise action models explicitly corrected.
Supervised learning aims in creating a model that takes into account the knowledge adapted by experience, and then uses it for evaluating new observations.