What is feature learning?
Feature learning is also referred to as machine learning. Here, learning based on features, representations and techniques comes to the fore. A system recognises and orients itself on these and associates correlations and classifications from them. Raw data thus serve as a basis and for further topics that build on them. Mechanical and mathematical Algorithms are processed and brought into context.
Learning always involves the absorption, processing and storage of data. Through the acquired knowledge, future actions as well as information outputs are specifically modified and also adapted. All experiences together form knowledge. At the same time, acting from experience also means that earlier sources of error and misinformation have already been filtered out.
How it works
In feature learning, so-called pattern recognition is an important feature, which is individually feasible, measurable as well as detectable. The distinction between features and characteristics plays a major role in pattern recognition. Carrying out subsequent processes on the basis of experience is the most important key point that takes place in machine learning.
Feature Learning vs. Deep Learning
Feature learning and deep learning are often mentioned together, although both types of learning have some differences. Machine learning is characterised by simple operations as well as processing. Deep Learning on the other hand, works with networks that are neuronal and artificial. These networks imitate the behavioural patterns such as learning and thinking of humans, for example. Human intervention is not necessary during the learning process, as the respective neuron network works independently. The positronic brain of the android Commander Data on the starship Enterprise consists of such a neuronal or neural network.
Feature learning as a subfield of AI
Feature Learning respectively Machine Learning (ML) represents a subfield of so-called artificial intelligence (AI). It is also considered a special form of applied computer science and mathematics. With the help of this form of learning, simple and also more complex IT systems can independently generate knowledge and recognise correlations from experience, depending on the purpose and information input. This in turn is used to create new data sets, previous results are either confirmed, discarded and, if necessary, corrected and supplemented.
Approaches
There are three algorithmic approaches:
- Unsupervised learning, also referred to as unsupervised learning.
- Supervised Learning, is also referred to as supervised learning
- Reinforcement Learning, is also referred to as reinforcement learning
Areas of application
Wherever more effective, more comfortable and faster work is necessary, machine learning is also taking place. Especially in the industrial and technology sector, where automated processes take place, feature learning is applied. Early warning systems, for example, react to impending damage, defects or failures. In medicine, machine learning supports doctors during operations and researchers in cancer and vaccine research.
Other areas of application:
- Book and film recommendations
- Various ranking systems
- Face and speech recognition
- Data analysis
- Ways of finding therapies for patients
- Customer Relationship Management
- Imaging analyses such as MRI or CT
- Defence against Internet threats
- Weather forecast
- Investigations and searches
- General analysis of customers' movement and purchasing behaviour
