MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -The goal of this search is to find the hypothesis that best fits the training examples. By selecting a hypothesis representation, the designer of the learning algorithm implicitly defines the space of all hypotheses that the program can ever represent and therefore can ever learn.

Detailed explanation-2: -A hypothesis is an explanation for something. It is a provisional idea, an educated guess that requires some evaluation. A good hypothesis is testable; it can be either true or false. In science, a hypothesis must be falsifiable, meaning that there exists a test whose outcome could mean that the hypothesis is not true.

Detailed explanation-3: -In a concept learning task, a human classifies objects by being shown a set of example objects along with their class labels. The learner simplifies what has been observed by condensing it in the form of an example. This simplified version of what has been learned is then applied to future examples.

Detailed explanation-4: -Definition. Learning can be viewed as a search through the space of all sentences in a concept description language for a sentence that best describes the data. Alternatively, it can be viewed as a search through all hypotheses in a hypothesis space.

Detailed explanation-5: -Concept learning describes the process by which experience allows us to partition objects in the world into classes for the purpose of generalization, discrimination, and inference. Models of concept learning have adopted one of three contrasting views concerning category representation.