Understanding Different Types of Machine Learning: Batch, Online, Instance-Based, and Model-Based Learning
Machine learning is an integral part of artificial intelligence (AI). It allows computer systems to learn from data and improve their performance. There are different types of machine learning, such as batch learning, online learning, example-based learning, and model-based learning. In this article, we will explore each of these types in detail and understand their unique characteristics.
Batch Learning
Batch learning is a type of machine learning where the system must acquire all the information it needs before it can start learning. In other words, the system cannot learn incrementally. To work with this type of learning, the first step is to train the system, which is always done offline. Once the system is trained, it can operate without learning.
batch learning requires significant time and resources to acquire all the necessary information. This type of learning is suitable for solving problems involving large amounts of data and the data does not change often.
Online Learning
Online learning is the opposite of batch learning. In this type of learning, the system can learn incrementally by providing the system with available data on a case-by-case or group-by-case basis. The system can then learn on the fly and quickly adapt to changes.
Online learning is suitable for problems that require a constant flow of information and that need to quickly adapt to changes. However, the learning speed of the system is critical. If the speed is high, the system learns quickly, but may forget old information.
Instance based learning
Instance-based learning is the simplest form of learning that is easy to understand. In this type of learning, the system learns from specific cases or examples. For example, if you use this type of learning in your email program, it will flag all emails that have been reported by users.
Instance-based learning algorithms include K-nearest neighbor, self-organizing map, learning weighted learning, and locally weighted learning.
Some of the Instance based learning algorithms:
- K nearest neighbor
- Self-organizing map
- Learning weighted learning
- Locally weighted learning
Model-based learning
Model-based learning is a type of learning that allows you to build models to make predictions. In this type of learning, the system learns from examples to build a model that can make predictions based on new information.
Conclusion
Machine Learning is an integral part of artificial intelligence and there are various learning methods such as group learning, online learning, example-based learning and model-based learning. The choice of learning method depends on the type of problem and the availability of information. Understanding different types of learning methods is important to effectively apply them to real-world problems.