Well posed Machine Learning
Well posed Machine Learning
Simply to say precisely it is nothing but learning by experience.
An agent or a Person solves a problem or Task T, Performance P, gain some experience E.
By doing this task T with performance P this agent will give some experience is denoted by E.
If P is the person measured at T it can improve E, with the help of Performance it will increase the experience.
If the performance is good, he will try to improve more good and become best.
If the performance is not good in that case he will understand law pols he will try to improve the experience he had gain through that, used some experience he adds some more new methodology to improve more that is well posed learning problem.
- Playing Checkers Problems
- Hand Written Recognition Problem
- Robot Driving Learning Problem
Let us see in all these there scenario what is the task T, what is the Performance P and what is the experience E that the learner will get some particular task.
Here if you can see that here the problem
Task 1 : what the user support to do
Task 2: how the user Performance should be their what is the user experience were is going to out of that.
Performance (P )
Playing Checkers Learning
|Playing against opponents to win the game|
If you are writing will be an exam, your task is finish all the questions in the given time in checkers need to win over the opponents
|Make perfect moves in order to win the game|
So that your Performance is good
If you are not making perfect moves so that your moves get wasted
Example: Candy Crush
|It plays itself to improve|
Handwriting Recognition Learning
|Classifying the images and Text|
|A database of homework test that means it have to some inbuilt database so that observing the observation of the data from the database it will learn itself on it will perform|
Robot Driving Learning Problem
|Drive the car in a four lane highway|
|Source to destination the average distance travelled should be longer and safer|
|Image vehicle on road|
Face Recognition Problem
|Redirecting different types of data|
|Measurable to predict maximum type of faces|
|Training machine with maximum amount of dataset of different face images|
To better filter emails as spam or not
|Classifying emails as spam or not|
|Measures the fraction of email accordingly classified as a spam or not|
|Observing you label email as spam or not|
Fruit prediction problem
|Forecasting different fruits for recognition|
|Measurable to predict maximum variety of fruits|
|Balancing the machine with a large datasets of fruits image|
Automatic translation of documents
|Translating one type language used in a document to other languages|
|Measurable to convert one language to another efficiency|
|Training machine with a large amount of dataset of different types of languages|