Machine learning

A gépi tanulás a mesterséges intelligencia egyik részterülete. A gépi tanulásos technológiák alkalmazásával fokozhatóak a szöveganalitikai szolgáltatások és megoldások hatékonysága.

What is machine learning?

Machine learning is a discipline within the concept of artificial intelligence (MI). In machine learning methods, the system sets up rules using input samples. Based on these rules (patterns), the system recognizes regularities in previously unknown datasets.

How does machine learning work?

Machine learning requires a large sample database to train the system. After the learning phase has been completed, the system will operate automatically. The success of the operation largely depends on the quantity and quality of the sample data.

What are the types of machine learning?

Supervised

A type of machine learning based on pre-classified learning data. This means that the expected output is given to the system by using teaching examples and the proper output.

Unsupervised

A type of machine learning that enables the algorithm to learn and find patterns with minimised human supervision and without learning data or predefined categories.

When machine learning is required?

Creating machine learning based systems is reasonable if the given task disproportionately time and resource consuming is and the solution provided is capable of operating efficiently.

What are the benefits of machine learning systems?

By using machine learning methods, the previously only manually fulfilled processes can be automated. However, it is important to test and refine such methods before the given solution is deployed and armed. The implementation is only worth in case of adquate efficiency. In such case, the system provides significant improvement considering time and work efficiency.

What do we use machine learning for?

Our algorithms are taught through learning data to produce an output that contains predictions based on the dataset. We are able to create models for the machine learning methods and to prepare the necessary data accordingly.
If you need more information about our solutions, please contact us.

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