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Pattern Classification, Chapter 1
1
Basic Probability

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Introduction
• Probability is the study of randomness and uncertainty.
• In the early days, probability was associated with games
of chance (gambling).

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Simple Games Involving Probability
Game: A fair die is rolled. If the result is 2, 3, or 4, you win
$1; if it is 5, you win $2; but if it is 1 or 6, you lose $3.
Should you play this game?

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Random Experiment
• a random experiment is a process whose outcome is uncertain.
Examples:
• Tossing a coin once or several times
• Picking a card or cards from a deck
• Measuring temperature of patients
• ...

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Sample Space
The sample space is the set of all possible outcomes.
Simple Events
The individual outcomes are called simple events.
Event
An event is any collection
of one or more simple events
Events & Sample Spaces

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Example
Experiment: Toss a coin 3 times.
• Sample space Ω
∀ Ω = {HHH, HHT, HTH, HTT, THH, THT, TTH, TTT}.
• Examples of events include
• A = {HHH, HHT,HTH, THH}
= {at least two heads}
• B = {HTT, THT,TTH}
= {exactly two tails.}

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Basic Concepts (from Set Theory)
• The union of two events A and B, A ∪ B, is the event consisting of
all outcomes that are either in A or in B or in both events.
• The complement of an event A, Ac
, is the set of all outcomes in Ω that
are not in A.
• The intersection of two events A and B, A ∩ B, is the event
consisting of all outcomes that are in both events.
• When two events A and B have no outcomes in common, they are
said to be mutually exclusive, or disjoint, events.

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Example
Experiment: toss a coin 10 times and the number of heads is observed.
• Let A = { 0, 2, 4, 6, 8, 10}.
• B = { 1, 3, 5, 7, 9}, C = {0, 1, 2, 3, 4, 5}.
• A ∪ B= {0, 1, …, 10} = Ω.
• A ∩ B contains no outcomes. So A and B are mutually exclusive.
• Cc
= {6, 7, 8, 9, 10}, A ∩ C = {0, 2, 4}.

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Rules
• Commutative Laws:
• A ∪ B = B ∪ A, A ∩ B = B ∩ A
• Associative Laws:
• (A ∪ B) ∪ C = A ∪ (B ∪ C )
• (A ∩ B) ∩ C = A ∩ (B ∩ C) .
• Distributive Laws:
• (A ∪ B) ∩ C = (A ∩ C) ∪ (B ∩ C)
• (A ∩ B) ∪ C = (A ∪ C) ∩ (B ∪ C)
• DeMorgan’s Laws:
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Venn Diagram
Ω
A BA∩B

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Probability
• A Probability is a number assigned to each subset (events) of a sample
space Ω.
• Probability distributions satisfy the following rules:

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Axioms of Probability
• For any event A, 0 ≤ P(A) ≤ 1.
• P(Ω) =1.
• If A1, A2, … An is a partition of A, then
P(A) = P(A1) + P(A2)+...+ P(An)
(A1, A2, … An is called a partition of A if A1 ∪ A2 ∪…∪An = A and A1, A2, …
An are mutually exclusive.)

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Properties of Probability
• For any event A, P(Ac
) = 1 - P(A).
• If A ⊂ B, then P(A) ≤ P(B).
• For any two events A and B,
P(A ∪ B) = P(A) + P(B) - P(A ∩ B).
For three events, A, B, and C,
P(A∪B∪C) = P(A) + P(B) + P(C) -
P(A∩B) - P(A∩C) - P(B∩C) + P(A∩B ∩C).

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Example
• In a certain population, 10% of the people are rich, 5% are famous,
and 3% are both rich and famous. A person is randomly selected
from this population. What is the chance that the person is
• not rich?
• rich but not famous?
• either rich or famous?

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Intuitive Development (agrees with axioms)
• Intuitively, the probability of an event a could be
defined as:
Where N(a) is the number that event a happens in n trialsWhere N(a) is the number that event a happens in n trials

Here We Go Again: Not So Basic
Probability

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More Formal:
• Ω is the Sample Space:
• Contains all possible outcomes of an experiment
• ω in Ω is a single outcome
• A in Ω is a set of outcomes of interest

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Independence
• The probability of independent events A, B and C is
given by:
P(A,B,C) = P(A)P(B)P(C)
A and B are independent, if knowing that A has happenedA and B are independent, if knowing that A has happened
does not say anything about B happeningdoes not say anything about B happening

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Bayes Theorem
• Provides a way to convert a-priori probabilities to a-
posteriori probabilities:

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Conditional Probability
• One of the most useful concepts!
AA
BB
ΩΩ

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Bayes Theorem
• Provides a way to convert a-priori probabilities to a-
posteriori probabilities:

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Using Partitions:
• If events Ai are mutually exclusive and partition Ω

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Random Variables
• A (scalar) random variable X is a function that maps
the outcome of a random event into real scalar
values
ωω
ΩΩ X(X(ωω))

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Random Variables Distributions
• Cumulative Probability Distribution (CDF):
• Probability Density Function (PDF):Probability Density Function (PDF):

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Random Distributions:
• From the two previous equations:

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Uniform Distribution
• A R.V. X that is uniformly distributed between x1 and
x2 has density function:
XX11 XX22

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Gaussian (Normal) Distribution
• A R.V. X that is normally distributed has density
function:
µµ

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Statistical Characterizations
• Expectation (Mean Value, First Moment):
•Second Moment:Second Moment:

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Statistical Characterizations
• Variance of X:
• Standard Deviation of X:

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Mean Estimation from Samples
• Given a set of N samples from a distribution, we can
estimate the mean of the distribution by:

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Variance Estimation from Samples
• Given a set of N samples from a distribution, we can
estimate the variance of the distribution by:

Chapter 1: Introduction to Pattern
Recognition (Sections 1.1-1.6)
• Machine Perception
• An Example
• Pattern Recognition Systems
• The Design Cycle
• Learning and Adaptation
• Conclusion

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Machine Perception
• Build a machine that can recognize patterns:
• Speech recognition
• Fingerprint identification
• OCR (Optical Character Recognition)
• DNA sequence identification

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An Example
• “Sorting incoming Fish on a conveyor according to
species using optical sensing”
Sea bass
Species
Salmon

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• Problem Analysis
• Set up a camera and take some sample images to extract
features
• Length
• Lightness
• Width
• Number and shape of fins
• Position of the mouth, etc…
• This is the set of all suggested features to explore for use in our
classifier!

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• Preprocessing
• Use a segmentation operation to isolate fishes from one
another and from the background
• Information from a single fish is sent to a feature
extractor whose purpose is to reduce the data by
measuring certain features
• The features are passed to a classifier

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• Classification
• Select the length of the fish as a possible feature for
discrimination

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The length is a poor feature alone!
Select the lightness as a possible feature.

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• Threshold decision boundary and cost relationship
• Move our decision boundary toward smaller values of
lightness in order to minimize the cost (reduce the number
of sea bass that are classified salmon!)
Task of decision theory

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• Adopt the lightness and add the width of the fish
Fish xT
= [x1, x2]
Lightness Width

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• We might add other features that are not correlated
with the ones we already have. A precaution should
be taken not to reduce the performance by adding
“noisy features”
• Ideally, the best decision boundary should be the one
which provides an optimal performance such as in the
following figure:

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• However, our satisfaction is premature because
the central aim of designing a classifier is to
correctly classify novel input
Issue of generalization!

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Lecture 3

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