Contents
- 1 What is Candidate-Elimination algorithm?
- 2 What is the key idea behind Candidate-Elimination algorithm *?
- 3 Which of the following is the bias associated with candidate elimination algorithm?
- 4 What is the smallest number of training examples you can provide so that the candidate elimination algorithm will perfectly learn the target concept?
- 5 What are the disadvantages of the version space algorithm?
- 6 What are the key properties of find-s algorithm?
What is Candidate-Elimination algorithm?
The candidate elimination algorithm incrementally builds the version space given a hypothesis space H and a set E of examples. The candidate elimination algorithm does this by updating the general and specific boundary for each new example. You can consider this as an extended form of Find-S algorithm.
What is the key idea behind Candidate-Elimination algorithm *?
The key idea in the Candidate-Elimination algorithm is to output a description of the set of all hypotheses consistent with the training examples. – Candidate-Elimination algorithm computes the description of this set without explicitly enumerating all of its members.
How do you overcome the delimits of Candidate-Elimination algorithm?
The Candidate Elimination Algorithm goes as follows –
- If d is a positive example.
- Remove from G any hypothesis that does not include.
- For each hypothesis s in S that does not include d, remove s from S.
- Add to S all minimal generalizations h of s such that h includes d, and.
- Some member of G is more general than h.
What is the key idea behind Candidate-Elimination algorithm * output a set of all hypothesis that are consistent with the training example output a set of general hypothesis output a set of specific hypothesis none of the above?
The Candidate-Elimination algorithm computes the version space containing all hypotheses from H that are consistent with an observed sequence of training examples.
Which of the following is the bias associated with candidate elimination algorithm?
inductive bias
hypothesis space H is identical to that of a deductive theorem prover utilizing the assertion “H contains the target concept.” This assertion is therefore called the inductive bias of the candidate elimination algorithm.
What is the smallest number of training examples you can provide so that the candidate elimination algorithm will perfectly learn the target concept?
Now assume you are a teacher, attempting to teach a particular target concept (e.g., 3 <= x <= 5, 2 <= y <= 9 ). What is the smallest number of training examples you can provide so that the Candidate-Elimination algorithm will perfectly learn the target concept?…
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What are the drawbacks of find s algorithm?
Limitations of Find-S Algorithm
- There is no way to determine if the hypothesis is consistent throughout the data.
- Inconsistent training sets can actually mislead the Find-S algorithm, since it ignores the negative examples.
What are the key properties of find s algorithm?
The find-S algorithm is a basic concept learning algorithm in machine learning. The find-S algorithm finds the most specific hypothesis that fits all the positive examples. We have to note here that the algorithm considers only those positive training example.
What are the disadvantages of the version space algorithm?
Disadvantages of the version space method: Inconsistent data (noise) may cause the target concept to be pruned….The number of hypotheses remaining is:
- 0 – no consistent description for the data in the language.
- 1 – answer (version space converges).
- 2+ – all descriptions in the language are implicitly included.