Learning For Everyonea) A – Training Set, B – Domain Set, C – Cross-Validation Set
b) A – Training Set, B – Test Set, C – Cross-Validation Set
c) A – Training Set, B – Test Set, C – Domain Set
d) A – Test Set, B – Domain Set, C – Training Set
Domain Set comprises of the total input data set. It is usually divided into a training set, a test set and a cross-validation set in the ratio 3:1:1. Since the learner learns about the data set from the training set, the later is usually larger than the test and cross-validation set.
a) true error
b) error of the classifier
c) training error
d) testing error
The learner only knows about the error it incurred over the training set instances. It is minimized by the learner to produce the labeling function. This is then used on the testing set to generate a testing error. The error produced by randomly selecting an instance from the dataset, and misclassifying it using the labeling function.
a) False
b) True
Sample complexity is the number of training examples required to converge to a successful hypothesis. It is given by m >= 1/e (ln |H| + ln (1/d)), where m is the number of training examples and H is hypothesis space.
a) Set of all hypotheses H
b) Both maximally general and maximally specific hypotheses
c) Maximally general hypothesis
d) Maximally specific hypothesis
Initially, only the maximally specific hypothesis is contained. That is generalized step by step after encountering every positive example. At any stage, the hypothesis is the most specific hypothesis consistent with training data.
a) False
b) True
Initially, set S contains only phi. It states that no example is positive. If there is no positive example in the dataset, the set will not change. Even after a complete iteration, S will remain the same and will contain only phi.
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