Semi-Supervised Learning: What is it?

During the Semi-Supervised Learning, a model is driven both with labeled and unstoppted data. The algorithm thus learns to recognize reasons in the data instances without known target variable, from a few data points labeled. This approach leads to more precise and effective modeling.

What is the Semi-Supervised Learning?

THE Semi-Supervised Learning (in French called « Semi-Supervised Learning » or « Semi-Encadré learning ») is a Hybrid approach in the field of automatic learningwhich combines the advantages of supervised and not supervised learning. This process uses a small amount of data labeled with a large amount of data not labeled to cause AI models. This allows the algorithm to detect patterns in the unseated data sets, supported by the labeled data. Thanks to this combined learning process, the model better understands the structure of unseated data, which leads to more precise predictions.

The principles of the Semi-Supervised Learning

The algorithms designed for semi-under-supervised learning start from several data hypotheses:

1. Continuity hypothesis: The points close to each other are more likely to have the same outing.
2. Regrouping hypothesis: The data can be divided into discreet groups, and the points within the same group probably share the same label.
3. Variety hypothesis: The data is approximately on a variety (set of linked points), which has a dimension below the input space. This hypothesis allows the use of distances and densities.

Semi-Supervised Learning: What is the difference with the Supervised and the Unsupervised Learning?

The supervised, the Unsupervised and the Semi-Supervised Learning are fundamental approaches to automatic learning (in English Machine learning). However, each of these methods has its own approach to bringing AI models. The table below shows the differences between semi-sub-supervised learning and traditional methods:

• THE Supervised Learningcalled « supervised learning » in French, only uses labeled data. This means that each example of data has a target variable or a known solution that the algorithm must predict. This approach is very precise, but requires large amounts of labeled data, often expensive and time -consuming to obtain.
• Not supervised learning (Unsupervised Learning) only uses unstructured data. The algorithm seeks to detect patterns or structures without predefined solution. This is useful when there are no labeled data, but may be less precise due to the lack of external reference points.
• THE Semi-Supervised Learning Combines these two approaches using a small amount of data labeled to understand the structure of a large quantity of unseated data. Semi-subupervised learning techniques change a supervised algorithm to integrate unsecured data into the model, which makes it possible to obtain precise predictions with relatively little marking efforts.

A practical example can illustrate the differences between these automatic learning approaches. If we take schoolchildren, we can do this analogy: supervised learning means that students are monitored at school and at home. If children have to learn alone, this is not supervised learning. On the other hand, when a concept is explained in class and deepened by homework, it is a semi-sub-supervised learning.

The Semi-Supervised Learning is a process in several stages:

1. Definition of objectives or problems: It is first necessary to clearly define the objectives or the purpose of the automatic learning model. The emphasis is placed on the question of what optimizations automatic learning must reach.
2. Data labeling: Certain unstructured data are labeled to provide aid to the learning algorithm. For the Semi-Supervised Learning to work, it is necessary to use data relevant to the formation of the model. For example, if an image classifier is trained to differentiate dogs and cats, car and trains images would not be appropriate.
3. Model training: Structured data are then used to teach the model what is its task and what results are expected.
4. Training with unseated data: After the model has learned with the drive data, the unselated data is integrated.
5. Model assessment and adjustment: To ensure that the model is working properly, assessments and adjustments are necessary. This process is repeated until the algorithm reaches the quality of desired results.

What are the advantages of the Semi-Supervised Learning?

Semi-underwater learning is particularly advantageous When a large volume of unstoppted data are available And that it would be too expensive or difficult to label them all. This is important because the formation of AI models traditionally requires a large number of labeled data providing the necessary context. For example, for a model for classification of images to distinguish two objects (such as a table and a chair) hundreds or even thousands of labeled training images are necessary. In addition, data labeling, as in the field of genetic sequencing, may require specific expertise.

Thanks to the Semi-Supervised Learning, it is possible toachieve great precision with a low number of data labeledbecause the labeled data sets strengthen unseated data. Structured data serve as a starting point, increasing the speed and precision of learning significantly. Thus, this approach makes it possible to maximize the potential of a small amount of data labeled while using a large number of unseated data, which increases theCost efficiency.

What are the main areas of application of the Semi-Supervised Learning?

Semi-Supervised Learning is now used in various fields, although classification tasks remain among its most common uses. Here is an overview of the main fields of application:

• Web content classification: Search engines like Google use semi-sub-supervised learning to assess the relevance of web pages compared to certain search requests.
• Classification of text and images: The objective here is to classify texts or images in one or more predefined categories. The semi-subupervised learning is particularly suitable for this, because there is a large amount of data not labeled, and the labeling of all data sets would be too long and expensive.
• Word analysis: Labeling audio files is also very expensive. The Semi-Supervised Learning offers a natural solution to this problem.
• Analysis of protein sequences: Due to the size of the DNA strands, semi-sub-supervised learning is ideal for analyzing the protein sequences.
• Detection of anomalies: The semi-subupervised learning makes it possible to detect unusual patterns that do not correspond to the standard.