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.
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.
