A
CONCEPT LEARNING TASK
•
Consider the example task of learning the
target concept "Days on which Tom enjoys his favourite
water sport”
Table: Positive and negative
training examples for the target
concept EnjoySport.
ü The task is to learn
to predict the value of EnjoySport for an arbitrary
day, based on the values of
its other attributes?
ü What hypothesis representation is provided to the learner?
·
Let’s
consider a simple representation in which each hypothesis consists of a conjunction of constraints on the
instance attributes.
·
Let each hypothesis be a vector of six constraints, specifying the
values of the six attributes
·
Sky
·
AirTemp
·
Humidity
·
Wind
·
Water
·
Forecast
•
For each attribute, the hypothesis will either
ü Indicate by a "?' that any value is acceptable for this attribute,
ü Specify a single required value (e.g., Warm) for
the attribute, or
ü Indicate by a "Φ" that no value is acceptable
•
If some instance x satisfies all the constraints of hypothesis
h, then h classifies x as a positive
example (h(x) = 1).
•
The hypothesis
that PERSON enjoys his favorite
sport only on cold days with high Humidity is represented by the expression
(?, Cold, High, ?, ?, ?)
•
The most general hypothesis -that every day is a positive example - is represented by
(?, ?, ?, ?, ?, ?)
•
The most specific possible hypothesis -that no day is a positive example -
is represented by
(Φ, Φ, Φ, Φ, Φ, Φ)
Notation
·
The set
of items over which the concept is defined is called the set of
instances, which is denoted by X.
Example: X is the set of all possible days, each
represented by the attributes: Sky, AirTemp, Humidity,
Wind, Water, and Forecast
·
The
concept or function to be learned is called
the target concept, which is denoted by c.
·
c can be any Boolean valued function defined over the instances X
c: X→ {O, 1}
•
Example: The target concept corresponds to the value of the attribute EnjoySport
(i.e., c(x) = 1 if EnjoySport = Yes, and c(x) = 0 if EnjoySport = No).
·
Instances
for which c(x) = 1 are called positive examples, or members
of the target concept.
·
Instances
for which c(x) = 0 are called negative examples, or non-members
of the target concept.
·
The ordered pair (x, c(x)) to describe the training example consisting
of the instance x and its target concept value c(x).
·
D to denote the set of available training examples.
ü
The symbol H to denote the set of all possible hypotheses that
the learner may consider regarding the identity of the target concept.
ü
Each hypothesis h in H represents a Boolean valued function
defined over X
h: X→{O, 1}
•
The goal
of the learner is to find a hypothesis h such that h(x) = c(x) for all x in X.
Concept
Learning Task: Notation
Ø
Given:
·
Instances X:
Possible days, each described by the attributes
ü Sky (with possible values Sunny, Cloudy, and Rainy),
ü AirTemp (with values Warm and Cold),
ü Humidity (with values Normal and High),
ü Wind (with
values Strong and Weak),
ü Water (with values Warm and Cool),
ü Forecast (with values Same and Change).
·
Hypotheses
H:
•
Each hypothesis is described by a conjunction of constraints on
the attributes Sky, AirTemp, Humidity,
Wind, Water, and Forecast.
•
The constraints may be "?" (any
value is acceptable) , “Φ” (no value is acceptable) , or a specific
value.
·
Target
concept c: EnjoySport : X → {0, l}
·
Training
examples D: Positive and negative examples of the target
function
•
Determine:
•
A
hypothesis h in H such that h(x) = c(x) for
all x in X.
Table: The EnjoySport concept learning task.
What is Machine Learning
•
“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,
ü this algorithm will produce a mathematical model andü with the help of the mathematical model,ü the machine will make a prediction andü take a decision without being explicitly programmed.
· Also,
during training data,
o
the more machine will work with
it
o
the more it will get experience and
o
the more efficient result is produced.
Example:
In Driverless Car,
the training data is fed to an Algorithm like
ü how
to Drive a Car on Highway, Busy and Narrow Street with
factors like
· speed
limit,
· parking,
· stop
at signals 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.
Definition: A computer program is said to
ü learn from experience E with respect to some class of tasks T and
performance measure P, if its performance at tasks in T, as measured
by P, improves with experience E.
To have a well-defined learning problem, three
features needs to be identified:
Simple learning process
•
For any learning system, we must be
knowing the three elements —
ü T
(Task),
ü P
(Performance Measure),
ü E
(Training Experience).
•
At a high level,
the process of learning
system looks like below fig.
The learning
process starts with
ü task T,
ü performance measure
P and
ü training experience
E and
ü objective are to
find an unknown target function.
The target
function is
ü an exact
knowledge to be learned from the training experience and
ü its
unknown.
For example, in a case of credit
approval,
•
the learning system will
have
ü customer
application records as experience and
ü task would be
to classify whether the given customer application is eligible
for a loan.
So in this case,
ü the training examples can be represented as
ü where
•
x represents customer
application details and
•
y represents the status of
credit approval.
•
With these
details, what is that exact knowledge to be learned from the training
experience?
•
So the target
function to be learned in the credit approval learning system is a mapping
function f:x →y.
•
This function
represents the exact knowledge defining the relationship
between input variable x and output variable y.
•
The learning
algorithms try to guess a “hypothesis’’ function h(X) that approximates
the unknown f(.).
•
A hypothesis is a function that best
describes the target and Hypothesis set or space H(.) is
the collection of all the possible legal hypothesis.
•
This is the
set from which the machine learning algorithm would determine the
best possible (only one) which would best describe the target
function or the outputs.
•
The goal of
the learning process is to find the final hypothesis that best approximates the
unknown target function.
Examples
i. Checkers game: A computer program that learns to play checkers might
improve its performance as measured by its ability to
win at the class of tasks involving playing checkers games, through
experience obtained by playing games against itself.
A checkers learning problem:
ii. A handwriting recognition learning problem:
iii. A robot
driving learning problem:
· Task T: driving on public four-lane highways using vision sensors
· Performance
measure P: average distance travelled before an error (as judged by human overseer)
· Training experience E: a sequence of images
and steering commands recorded
while observing a human driver
