In organic chemistry, the ability to classify novel members of a family of molecules (e.g., esters) from their chemical structure requires the chemist to orient on the relevant part of the structure which is invariant for all members of a family and ignore the parts that change from example to example.
For example, we provide concrete examples to explain how these cryptographic solutions can be deployed.
To improve the training process, human experts are selectively given training examples to label while other training examples are automatically labeled by the machines [19, 21].
We describe exemplars from an information literacy module and provide an analysis of these examples to illustrate how each example addresses Whole Brain® learning, facilitating of learning and assessment.
Although there are some difficulties (like constructing the suitable examples or counter examples to show the existence), the semi-infinite programming problems involving the generalized invex functions are very interesting.
There have however been examples to the contrary, among them examples of literal "atheists in foxholes".
Given this consideration, we also used the probabilistic measure on the training examples to remove training examples with ambiguous labels.
This final filtering step reduced our test data from 8315 examples to 7623 examples (4591 negative and 3032 positive) for comparing our method with these alternatives.
Students preferred examples to explanations even when the examples were brief and the explanations were detailed [ 45].
Common principles derived from these research areas emphasize the importance of: a) learning with examples of a particular case, b) providing multiple examples, c) linking examples to underlying principles and d) prompting learners to make connections between examples and principles [ 9].
