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Well posed learning problems

  • Difficulty Level : Basic
  • Last Updated : 22 Jan, 2021

Well Posed Learning Problem – A computer program is said to learn from experience E in context to some task T and some performance measure P, if its performance on T, as was measured by P, upgrades with experience E. 

Any problem can be segregated as well-posed learning problem if it has three traits – 

  • Task
  • Performance Measure 
  • Experience 

Certain examples that efficiently defines the well-posed learning problem are – 

1. To better filter emails as spam or not 

  • Task – Classifying emails as spam or not
  • Performance Measure – The fraction of emails accurately classified as spam or not spam 
  • Experience – Observing you label emails as spam or not spam 

2. A checkers learning problem

  • Task – Playing checkers game 
  • Performance Measure – percent of games won against opposer
  • Experience playing implementation games against itself

3. Handwriting Recognition Problem 

  • Task – Acknowledging handwritten words within portrayal 
  • Performance Measure – percent of words accurately classified
  • Experience – a directory of handwritten words with given classifications

4. A Robot Driving Problem 

  • Task – driving on public four-lane highways using sight scanners
  • Performance Measure – average distance progressed before a fallacy
  • Experience – order of images and steering instructions noted down while observing a human driver

5. Fruit Prediction Problem

  • Task – forecasting different fruits for recognition
  • Performance Measure – able to predict maximum variety of fruits
  • Experience – training machine with the largest datasets of fruits images

6. Face Recognition Problem

  • Task – predicting different types of faces
  • Performance Measure – able to predict maximum types of faces
  • Experience – training machine with maximum amount of datasets of different face images

7. Automatic Translation of documents

  • Task – translating one type of language used in a document to other language
  • Performance Measure – able to convert one language to other efficiently
  • Experience – training machine with a large dataset of different types of languages

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