Version Space and List-Then-Eliminate Algorithm

Consistent Hypothesis, Version Space and List-Then-Eliminate Algorithm

An hypothesis h is said to be consistent hypothesis with a set of training examples D iff  h(x) = c(x) for each example in D,

Video Tutorial on Consistent Hypothesis, Version Space and List-Then-Eliminate Algorithm

For Example:

ExampleCitationsSizeInLibraryPriceEditionsBuy
1SomeSmallNoAffordableOneNo
2ManyBigNoExpensiveManyYes

h1 = (?, ?, No, ?, Many) – Consistent Hypothesis as it is consistent with all the training examples

h2 = (?, ?, No, ?, ?) – Inconsistent Hypothesis as it is inconsistent with first training example

Version Space

The version space VSH,Dis the subset of the hypothesis from H consistent with the training example in D,

List-Then-Eliminate algorithm

Steps in List-Then-Eliminate Algorithm

1. VersionSpace = a list containing every hypothesis in H

2. For each training example, <a(x), c(x)> Remove from VersionSpace any hypothesis h for which h(x) != c(x)

3. Output the list of hypotheses in VersionSpace.

Example:

F1  – > A, B

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F2  – > X, Y

Here F1 and F2 are two features (attributes) with two possible values for each feature or attribute.

Instance Space: (A, X), (A, Y), (B, X), (B, Y) – 4 Examples

Hypothesis Space: (A, X), (A, Y), (A, ø), (A, ?), (B, X), (B, Y), (B, ø), (B, ?), (ø, X), (ø, Y), (ø, ø), (ø, ?), (?, X), (?, Y), (?, ø), (?, ?)  – 16 Hypothesis

Semantically Distinct Hypothesis : (A, X), (A, Y), (A, ?), (B, X), (B, Y), (B, ?), (?, X), (?, Y (?, ?), (ø, ø) – 10

List-Then-Eliminate Algorithm Steps

Version Space: (A, X), (A, Y), (A, ?), (B, X), (B, Y), (B, ?), (?, X), (?, Y) (?, ?), (ø, ø), •Training Instances

F1  F2  Target

A  X      Yes

A  Y      Yes

Consistent Hypothesis are (Version Space):   (A, ?), (?, ?)

Problems with List-Then-Eliminate Algorithm

The hypothesis space must be finite

Enumeration of all the hypothesis, rather inefficient

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Summary

This tutorial discusses the Consistent Hypothesis, Version Space, and List-Then-Eliminate Algorithm in Machine Learning. If you like the tutorial share with your friends. Like the Facebook page for regular updates and YouTube channel for video tutorials.

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