https://github.com/anuraganalog/concept-learning

My Concept Learning algorithms implementation.
https://github.com/anuraganalog/concept-learning

candidate-elimination concept-learning dataset find-s python3

Last synced: 3 days ago
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My Concept Learning algorithms implementation.

• Host: GitHub
• URL: https://github.com/anuraganalog/concept-learning
• Owner: AnuragAnalog
• License: apache-2.0
• Created: 2020-11-16T14:03:28.000Z (about 4 years ago)
• Default Branch: master
• Last Pushed: 2020-11-21T14:22:10.000Z (about 4 years ago)
• Last Synced: 2024-12-20T14:59:03.115Z (19 days ago)
• Topics: candidate-elimination, concept-learning, dataset, find-s, python3
• Language: Python
• Homepage:
• Size: 13.7 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Concept Learning

App is hosted at this [link](concept-learning.herokuapp.com)

## Find S

1. Initialize h to the most specific hypothesis in H

2. For each positive training instance x

    * For each attribute constraint ai in h

        * If the constraint ai in h is satisfied by x

        * Then do nothing

        * Else replace ai in h by the next more general constraint that is satisfied by x

3. Output hypothesis h

## Candidate Elimination

G <- maximally general hypotheses in H

S <- maximally specific hypotheses in H

For each training example d, do 

* If d is a positive example

    * Remove from Gany hypothesis inconsistent with d

    * For each hypothesis s in S that is not consistent with d 

        * Remove s from S

        * Add toSall minimal generalizations h of s such that

            1. h is consistent with d, and

            2. some member of G is more general than h

        * Remove from S any hypothesis that is more general than another hypothesis in S

* If d is a negative example

    * Remove from S any hypothesis inconsistent with d

    * For each hypothesis g in Gthat is not consistent with d

        * Remove g from G

        * Add to G all minimal specializations h of g such that

            1. h is consistent with d, and

            2. some member of S is more specific than h

        * Remove from G any hypothesis that is less general than another hypothesis in G