Examples of 'supervised learning' in a sentence
Meaning of "supervised learning"
In the context of machine learning, supervised learning is a type of algorithm where the model learns from a labeled dataset, where both input and expected output are provided. The model uses this labeled data to make predictions or classifications on new, unseen data
How to use "supervised learning" in a sentence
Basic
Advanced
supervised learning
Supervised learning depends on previously labeled data.
I will talk about the supervised learning.
Supervised learning algorithms include classification and regression.
This is the case for supervised learning.
How supervised learning algorithms work.
It is a visualisation tool mainly used in supervised learning.
Supervised learning itself comes in two different flavors.
There are many techniques for supervised learning.
Supervised learning from human experts.
So this is an example of a supervised learning algorithm.
Supervised learning involves learning from a training set of data.
Let us start with supervised learning.
A robot and supervised learning algorithms for physical rehabilitation.
A perceptron is an algorithm used in supervised learning of binary classifiers.
The supervised learning problem.
See also
Now take a look at some supervised learning algorithms.
Logistic regression is a common classification method in supervised learning.
This distinguishes unsupervised learning from supervised learning.
Classification algorithms usually require a supervised learning stage.
An example of supervised learning is spam filtering.
Bayesian spam filtering is a common example of supervised learning.
Two preferred supervised learning algorithms are described below.
Backpropagation is an example of a supervised learning strategy.
Supervised learning and classification.
A known example of supervised learning is the spam filter.
This is the core ideology behind supervised learning.
Underneath supervised learning we have classification and regression problems.
The first is called supervised learning.
The theory of supervised learning has significantly advanced in recent decades.
All the processes are solved in a supervised learning framework.
Then supervised learning proceeds from only the labeled examples.
This is the general theory behind supervised learning.
This type of iterative supervised learning is called active learning.
There are various other algorithms for supervised learning.
That is an explanation of supervised learning and unsupervised learning.
There are many algorithms which build supervised learning.
This method is known as supervised learning or learning with teacher.
Neural networks are generally algorithms that proceed by supervised learning.
Hybrid learning combines supervised learning and unsupervised learning.
The alignment between the two is done through supervised learning.
See also supervised learning.
In artificial intelligence they are called supervised learning models.
This is a supervised learning algorithms make predictions based on a set of examples.
These may be applied as inputs to a supervised learning algorithm.
A supervised learning algorithm looks for patterns in those value labels.
It is totally different from supervised learning.
Supervised learning and regression have been used to build models of poses.
Particularly talking about supervised learning.
Beyond supervised learning.
This is then referred to as supervised learning.
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