Machine Learning Mid - I, September - 2014

1.Machine learning is inherently a multidisciplinary field.
  • Inter disciplinary
  • Multi-disciplinary
  • single
  • None
Answer: B
2.A computer program is said to learn from __________E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.
  • Training
  • Experience
  • Database
  • Algorithm
Answer: A
3.__________methods have been used to train computer-controlled vehicles to steer correctly when driving on a variety of road types.
  • Machine Learning
  • Data Mining
  • Neural networks
  • Robotics
Answer: A
4.Any hypothesis found to approximate the target function well over a sufficiently large set of training examples will also approximate the target function well over other unobserved examples
  • Hypothesis
  • Inductive Hypothesis
  • Learning
  • Concept Learning
Answer: B
5.The _________algorithm computes the version space containing all hypotheses from H that are consistent with an observed sequence of training examples.
  • Inductive Hypothesis
  • Artificial Neural Network
  • Candidate Elimination
  • none
Answer: C
6.The _________, denoted VSHVD, with respect to hypothesis space Hand training examples D, is the subset of hypotheses from H consistent with the training examples in D.
  • Space
  • Vertical space
  • version space
  • version spain
Answer: C
7.Quinlan and Rivest (1989) describe experiments applying the MDL principle to choose the _______ for a decision tree.
  • Best size
  • big size
  • Small size
  • over fit
Answer: A
8.Minimum Description Principle is a version of _________that can be interpreted within a Bayesian Network.
  • ID3
  • Selection measure
  • occam’s razor
  • PAC
Answer: C
9.A perceptron takes a vector of real-valued inputs, calculates a linear combination of these inputs, then outputs ____________
  • 1 or -1
  • 0 or 1
  • -1 or 0
  • none
Answer: A
10.If the training examples are not linearly separable, the delta rule converges toward a approximation to the target concept.
  • Over fit
  • under fit
  • doesn’t fit
  • best fit
Answer: D
11.Theoretical results have been developed that characterize the fundamental relationship among the number of __________________ examples observed.
Answer: Training
12.The __________of L is any minimal set of assertions B such that for any target concept c and corresponding training examples Dc.
Answer: Inductive Bias
13.To apply MDL principle in practice we must choose _______________appropriate for the given learning task.
Answer: Specific encodings or representations
14._____________________Prefer the simplest hypothesis that fits the data.
Answer: Occam’s razor
15._____________is a significant practical difficulty for decision tree learning and many other learning methods.
Answer: Overfitting
16.One successful method for finding high accuracy hypotheses is a technique called___________.
Answer: Post-pruning
17.__________learning methods provide a robust approach to approximating real-valued, discretevalued, and vector-valued target functions.
Answer: Neural network
18.In learning to play checkers, the system might learn from ________training examples consisting of individual checkers board states and the correct move for each.
Answer: Direct
19.learning algorithms to acquire only some approximation to the target function, and for this reason the process of learning the target function is often called __________________.
Answer: Function approximation
20.PAC Acronym ___________________________________.
Answer: Probably Approximately Correct