Aspects of developing a learning system: training data, concept representation, function approximation

According to Arthur Samuel “Machine Learning enables a Machine to Automatically learn from Data, Improve performance from an Experience and predict things without explicitly programmed.”

In Simple Words, When we fed the Training Data to Machine Learning Algorithm, this algorithm will produce a mathematical model and with the help of the mathematical model, the machine will make a prediction and take a decision without being explicitly programmed. Also, during training data, the more machine will work with it the more it will get experience and the more efficient result is produced.

The following are the different aspects of developing a learning system. Let us consider designing a program to learn to play checkers, with the goal of entering it in the world checkers tournament.

Example :  In Driverless Car, the training data is fed to Algorithm like how to Drive Car in Highway, Busy and Narrow Street with factors like speed limit, parking, stop at signal etc. After that, a Logical and Mathematical model is created on the basis of that and after that, the car will work according to the logical model. Also, the more data the data is fed the more efficient output is produced.

Designing a Learning System in Machine Learning :

According to Tom Mitchell, “A computer program is said to be learning from experience (E), with respect to some task (T). Thus, the performance measure (P) is the performance at task T, which is measured by P, and it improves with experience E.”

Example: In Spam E-Mail detection,

• Task, T: To classify mails into Spam or Not Spam.
• Performance measure, P: Total percent of mails being correctly classified as being “Spam” or “Not Spam”.
• Experience, E: Set of Mails with label “Spam”

Checkers learning problem

• Task T: playing checkers
• Performance measure P: percent of games won against opponents
• Training experience E: playing practice games against itself

In order to complete the design of the learning system, we must now choose

• The exact type of knowledge to be learn
• A representation for this target knowledge
• A learning mechanism

1. Choosing the training experience

• The first design choice is to choose the type of training experience from which the system will learn.
• The type of training experience available can have a significant impact on success or failure of the learner.

There are three attributes which impact on success or failure of the learner

1. Whether the training experience provides direct or indirect feedback regarding the choices made by the performance system
2. The degree to which the learner controls the sequence of training examples
3. How well it represents the distribution of examples over which the final system performance P must be measured

 2. Choosing the Target Function

The next important step is choosing the target function. It means according to the knowledge fed to the algorithm the machine learning will choose NextMove function which will describe what type of legal moves should be taken. For example : While playing chess with the opponent, when opponent will play then the machine learning algorithm will decide what be the number of possible legal moves taken in order to get success.

NextMove: B-->M

This function accepts as input any board from the set of legal board states B and produces as output some move from the set of legal moves M.

An alternative target function and one that will turn out to be easier to learn in this setting is an evaluation function that assigns a numerical score to any given board state.

V: B-->R

Denote that V maps any legal board state from the set B to some real value

Let us therefore define the target value V(b) for an arbitrary board state b in B, as follows

1.  if b is a final board state that is won, then V(b) =100
2.  if b is a final board state that is lost, then V(b) = -100
3. if b us a final board state that is drawn, then V(b) =0
4. if b is a not a final state in the gae, then V(b) = V(b’), where b’ is the best final board state that can be achieved starting from b and playing optimally until the end of the game. 

3. Choosing a representation for the target function

We need to choose a representation that the learning algorithm will use to describe the function NextMove. The function NextMove will be calculated as a linear combination of the following board features:

• xl: the number of black pieces on the board
• x2: the number of red pieces on the board
• x3: the number of black kings on the board
• x4: the number of red kings on the board
• x5: the number of black pieces threatened by red (i.e., which can be captured on red's next turn)
• x6: the number of red pieces threatened by black

NextMove = u0 + u1x1 + u2x2 + u3x3 + u4x4 + u5x5 + u6x6

Here u0, u1 up to u6 are the coefficients that will be chosen(learned) by the learning algorithm

4. Choose a Function Approximation Algorithm

To learn the target function NextMove, we require a set of training examples, each describing a specific board state b and the training value (Correct Move ) y for b. The training algorithm learns/approximate the coefficients u0, u1 up to u6 with the help of these training examples by estimating and adjusting these weights.

For Example: When a training data of Playing chess is fed to algorithm so at that time it is not machine algorithm will fail or get success and again from that failure or success it will measure while next move what step should be chosen and what is its success rate.

5. The final design

The final design is created at last when system goes from number of examples, failures and success, correct and incorrect decision and what will be the next step etc.