Semi-Supervised Learning Explained

What is semi-supervised learning?

Machine learning has become a popular topic that feeds people’s fascination as they marvel at what computer algorithms can do after proper training. Some well-known applications include data mining, which involves extracting new information from existing data, or using algorithms to classify images based on the things contained within them.

The
algorithms used for machine learning get trained through one of three ways:
supervised, unsupervised and semi-supervised learning. Here are some of the
things people should know about each method.

Supervised Learning Involves Sets of Labeled Data

Supervised
learning is the most common way to train a machine learning algorithm. A set of
labeled data is used with this method. The algorithm attempts to understand the
difference between the input (untrained) data and the tagged information, which
is the output data. When it can do that with sufficient accuracy, it’s
thoroughly trained.

There
are two categories
for supervised learning. With regression, the algorithm predicts a numerical value based on
the information it already knows. For example, it could work well when guessing
height and weight or making predictions about the stock market.

Alternatively,
people can use the second category, which is classification. Then, the
algorithm assesses the input data and determines how to group it with similar
items. In that case, the input data might be a picture of a horse, and the
algorithm would recognize it as an animal rather than a piece of furniture.

There’s No Labeled Data in Unsupervised Learning

When
using unsupervised data in machine learning, people cannot train the algorithms
in the same way described above. That’s because the output data is unknown, and
therefore, unlabeled. If a company is evaluating supervised and unsupervised
learning in data mining, that approach could work well in some instances, like
determining the target market for a product that hasn’t launched yet.

Since the new algorithm can’t rely on a data set for training, it must detect patterns in the information independently. In most cases, supervised learning is more applicable to real-world problems due to the known output values. When people working with a machine learning algorithm don’t have the outputs, they don’t have an accurate way to assess its accuracy.

In
contrast, people can look at a machine learning algorithm trained through
supervised learning and compare the information with the training data set with
the results.

With that in mind, it’s still useful for people to know about a type of unsupervised learning called clustering. It assigns data points into appropriate groups based on the level of similarity or dissimilarity between them.

One possible way to use this type of training is when applying machine learning to the cybersecurity field and preparing the algorithm to offer anomaly detection across a network. It could help spot previously unseen threats.

What About Semi-Supervised Learning?

The
most straightforward way to define semi-supervised learning is to discuss it as
something that
falls between
supervised and unsupervised learning. Generally, there is a large set of
unlabeled data and a smaller set of labeled information. The labeled collection
helps improve the overall results of the training since there are some known
factors.

People sometimes prefer using the semi-supervised method of training algorithms for a couple of reasons. Labeling all the data before beginning to train the algorithm can be a time- and cost-intensive process.

Moreover, since humans have to choose the labels for the training data set, they could unintentionally introduce elements of bias to the algorithm as it’s exposed to those hidden preferences during training.

Website
classification and speech recognition are two potential ways to use
semi-supervised learning. In one recent example, researchers working with the
Alexa assistant for Amazon smart speakers cut speech recognition errors by as much as 22% by using semi-supervised
learning. The team used 1 million hours of unlabeled data and 7,000 hours of
labeled information.

One
of the advantages people might notice by using this method is that it might
create a new category for data that the humans responsible for labeling didn’t
think of earlier.

No Single Best Option to Choose

Whether
people are curious about supervised and unsupervised learning in data mining or
want to understand more about why taking the semi-supervised approach might be
the best option, they should realize that none of these methods is the most
appropriate one in every case.

Instead,
it’s necessary for individuals to assess the specifics of their projects and
take other factors into account, such as their budget and schedule. Then, it
should be easier for them to pick which learning method to use.