A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance in tasks T, as measured by P, improves with experience E.
- The class of tasks (T)
- The measure of performance to be improved (P)
- The source of experience (E)
- Task (T): Playing Checkers
- Performance Measure (P): Percent of games won against opponents.
- Training Experience (E): Playing practice games against itself.
- Task (T): Recognizing and classifying handwritten words within images.
- Performance Measure (P): Percent of words correctly classified.
- Training Experience (E): A dataset of handwritten words with given classifications.
- Task (T): Driving on public four-lane highways using vision cameras.
- Performance Measure (P): Average distance travelled before an error (as judged by a human observer).
- Training Experience (E): A sequence of images and steering commands recorded while observing a human driver.
This tutorial is originally published at - https://www.asquero.com/article/well-defined-learning-problem/
Different Types of Machine Learning Algorithms - https://www.asquero.com/article/different-types-of-machine-learning-algorithms
Advantages and Disadvantages of different types of machine learning algorithms - https://www.asquero.com/article/advantages-and-disadvantages-of-different-types-of-machine-learning-algorithms
Advantages and Disadvantages of Machine Learning - https://www.asquero.com/article/advantages-and-disadvantages-of-machine-learning