According to what we know about inductive and connectionist learning, what is the difference between them ?

For those who do not know about :

Inductive Learning, like what we have in decision tree and make a decision based on amount of samples

Connectionist Learning, like what we have in artificial neural network


All of the statistical learning is about inductive learning.

What is the difference between inductive learning and connectionist learning?

Inductive learning is about identifying patterns from examples. It is more related to statistics. Connectionist learning is more about finding a common pattern and predicting as well as self-learning(learning from the experience of prediction).

Connectionist learning is where learning occurs by modifying connection strengths based on experience. This is not the case with inductive learning. In inductive learning, we are not modifying things based on experience. Inductive learning just finds common patterns, not self-learning based on experience.

Learning requires both practice and rewards

In inductive learning, we learn the model from raw data (so-called training set), and in the deductive learning, the model is applied to predict the behaviour of new data.

Connectionist Learning is a group of inductive learning and deductive learning.

  • $\begingroup$ There several things I don't like about this answer. First, you use the term "self-learning", without providing a definition of this non-standard term. Second, you say that IL is "identifying patterns from examples" and then you say that CL is "more about finding a common pattern and predicting". This distinction is not clear because you're almost saying the same thing. The sentence "In inductive learning, we are not modifying things based on experience" is also wrong. See en.wikipedia.org/wiki/Inductive_reasoning. There are other problems. $\endgroup$ – nbro Dec 19 '19 at 15:17

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