What is Batch, Online, Instance based and Model based Learning?
In this kind of Machine learning system, the system cannot learn incrementally: the system must obtain all the needed data. That means it’ll require many resources and an enormous amount of your time, so it’s always done offline. So, to figure with this sort of learning, the primary thing to try to coach (train) the system, then launch it with without learning.
Online Learning
this type of learning is the opposite of batch learning. It means the system can learn incrementally by providing the system with all the available data as instances (groups or individually), and then the system can learn on the fly.
You can use this sort of system for problems that need the continual flow of knowledge, which also must adapt quickly to any changes. Also, you’ll use this sort of system to figure with very large data sets
You should know that how fast your system can adapt to any changes in the “learning rate” of data. If the speed is high, it means that the system will learn quickly, but it will also forget old data quickly.
Instance based learning
this is the simplest type of learning that we should learn by heart. By using this sort of learning in our email program, it’ll flag all of the emails that were flagged by users.
Some of the Instance based learning algorithms:
- K nearest neighbor
- Self-organizing map
- Learning weighted learning
- Locally weighted learning
Model-based learning
There is another type of learning in which learning from example allows construction to make predictions.
