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In many machine learning settings, labeled examples are difficult to collect while unlabeled data are abundant.
Documented examples are difficult to find, which is not surprising since the policy constraint can be expected to operate by way of the mechanism of anticipated reaction.
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Such regulatory learning from others' examples is difficult, being more bounded than rational and subject to biases [ 38].
Other secondary structures, like triple helices for example, are difficult to predict bioinformatically at present.
Cardiac surgery volume benchmarks, for example, are difficult to meet as a result of improved percutaneous coronary interventions such as stenting [ 20].
This implies that indicators about quality of services, for example, are difficult to include in our list of indicators unless we can formulate a clearly measurable standard for such quality.
The importance of André's example is difficult to exaggerate.
Jumping to "Comfortably Numb," for example, is difficult without knowing it's chapter 21.
To generalize from a single example is difficult, especially when the example itself is changing, growing, and evolving.
Because shopping for a mortgage, for example, is difficult and tedious, a site offering straightforward and easy-to-understand comparisons could be a hit.
Forward movement in concentrically banded snakes, for example, is difficult to perceive when the animal moves between reeds or long blades of grass.
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