mtr607 chapter 0
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Learning Algorithm and
Neural Networks
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MTR 607
Textbook:Simon Haykin, Neural Networks A Comprehensive
Foundation, 2nd Ed., 1999
Lecturer:Dr. Alaa Sagheer
Place:Seminar Room, E-JUST
Grading: Class participation (10%),
Assignments and reports (20%),
Midterm test (30%),
Final exam (40%)
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Course OverviewIntroduction to Artificial Neural Networks,
Artificial and human neurons (Biological Inspiration)
The learning process,
Supervised and unsupervised learning,
Reinforcement learning,
Applications Development and Portfolio
The McCulloch-Pitts Model of Neuron,
A simple network layers, Multilayer networks
Perceptron,
Back propagation algorithm,
Recurrent networks,Associative memory,
Self Organizing maps,
Support Vector Machine and PCA,
Applications to speech, vision and control problems.
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ANNs ResourcesMain text books:
Neural Networks: A Comprehensive Foundation
, S. Haykin(very good -
theoretical)
Pattern Recognition with Neural Networks, C. Bishop (very good-more accessibleNeural Network Design by Hagan, Demuth and Beale (introductory)
Books emphasizing the practical aspects:
Neural Smithing, Reeds and MarksPractical Neural Network Recipees in C++ T. Masters
Seminal Paper:
Parallel Distributed Processing Rumelhart and McClelland et al.
Other:
Neural and Adaptive Systems, J. Principe, N. Euliano, C. Lefebvre
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Review Articles:R. P. Lippman, An introduction to Computing with Neural Nets IEEE ASPMagazine, 4-22, April 1987.
T. Kohonen, An Introduction to Neural Computing, Neural Networks, 1, 3-16, 1988.A. K. Jain, J. Mao, K. Mohuiddin, Artificial Neural Networks: A Tutorial IEEEComputer, March 1996 p. 31-44.
ANNs Resources
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Course OverviewIntroduction to Artificial Neural Networks,
Artificial and human neurons (Biological Inspiration)The learning process,
Supervised and unsupervised learning,
Reinforcement learning,
Applications Development and Portfolio
The McCulloch-Pitts Model of Neuron,A simple network layers, Multilayer networks
Perceptron,
Back propagation algorithm,
Recurrent networks,
Associative memory,Self Organizing maps,
Support Vector Machine and PCA,
Applications to speech, vision and control problems.
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Part I:
1. Artificial Neural Networks
2. Artificial and human neurons (Biological Inspiration)
3. Tasks & Applications of ANNs
Part II:
1. Learning in Biological Systems
2. Learning with Artificial Neural Networks
Introduction to Artificial Neural Networks
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ANNs vs. ComputersDigital Computers
Analyze the problem to be solved
Deductive Reasoning. We applyknown rules to input data to produce
output.
Computation is centralized,synchronous, and serial.
Not fault tolerant. One transistor goesand it no longer works.
Static connectivity.
Applicable if well defined rules withprecise input data.
8MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer
Artificial Neural Networks
No requirements of an explicit
description of the problem.
Inductive Reasoning. Given input and
output data (training examples), we
construct the rules.
Computation is collective,asynchronous, and parallel.
Fault tolerant and sharing of
responsibilities.
Dynamic connectivity.
Applicable if rules are unknown or
complicated, or if data are noisy or
partial.
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What is ANN?
9MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer
ANN is a branch of "Artificial Intelligence". It is a system modeled based on the human brain.ANN goes by many names, such as connectionism, parallel distributed processing, neuro-computing, machine learning algorithms, and finally, artificial neural networks.
Developing ANNs date back to the early 1940s. It experienced a wide popularity in the late
1980s. This was a result of the discovery of new techniques and developments in PCs.
Some ANNs are models of biological neural networks and some are not.
ANN is a processing device (An algorithm or Actual hardware) whose design was motivated by
the design and functioning of human brain.
Inside ANN:
ANNs design is what distinguishes neural networks from other mathematical techniquesANN is a network of many simple processors ("units or neurons), each unit has a smallamount of local memory.
The units are connected by unidirectional communication channels ("connections"), whichcarry numeric (as opposed to symbolic) data.
The units operate only on their local data and on the inputs they receive via the connections.
Artificial Neural Networks (1)
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ANNs Operation
ANNs normally have great potential for parallelism (multiprocessor-friendly architecture),
since the computations of the units are independent of each other. Same like biological neuralnetworks.
Most neural networks have some kind of "training" rule whereby the weights of connections areadjusted on the basis of presented patterns.
In other words, neural networks "learn" from examples, just like childrenand exhibit somestructural capability for generalization.
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Artificial Neural Networks (2)
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ANNs are a powerful technique (Black Box) to solve many real world
problems. They have the ability to learn from experience in order toimprove their performance and to adapt themselves to changes in the
environment.
In addition, they are able to deal with incomplete information or
noisy data and can be very effective especially in situations where it is
not possible to define the rules or steps that lead to the solution of aproblem.
Once trained, the ANN is able to recognize similarities when
presented with a new input pattern, resulting in a predicted output
pattern.
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Artificial Neural Networks (3)
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What can a ANN do?
Compute a known function
Approximate an unknown function
Pattern Recognition
Signal Processing
.Learn to do any of the above
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Part I:
1. Artificial Neural Networks (ANNs)
2. Artificial and human neurons (Biological Inspiration)
3. Tasks & Applications of ANNs
Part II:
1. Learning in Biological Systems
2. Learning with Artificial Neural Networks
Introduction to Artificial Neural Networks
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Biological Inspiration
Animals are able to react adaptively to changes in their external
and internal environment, and they use their nervous system toperform these behaviours.
An appropriate model/simulation of the nervous system shouldbe able to produce similar responses and behaviours in artificialsystems.
The nervous system is build by relatively simple units, theneurons, so copying their behaviour and functionality should bethe solution!
Biological Neural Networks (BNN) are much more
complicated in their elementary structures than the
mathematical models we use for ANNs
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ANN as a model of brain-
like Computer
Brain
The human brain is still not well
understood and indeed its behavior
is very complex!
There are about 10-11 billion
neurons in the human cortex each
connected to , on average, 10000others. In total 60 trillion synapses
of connections.
The brain is a highly complex,
nonlinear and parallel computer
(information-processing system)
ANN as a Brain-Like Computer
15MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer
An artificial neural network (ANN) is
a massively parallel distributed processor
that has a natural propensity for storingexperimental knowledge and making it
available for use. It means that:
Knowledge is acquired by the network
through a learning (training) process;
The strength of the interconnections
between neurons is implemented by
means of the synaptic weights used to
store the knowledge.
The learning process is a procedure of theadapting the weights with a learning
algorithm in order to capture the knowledge.
On more mathematically, the aim of the
learning process is to map a given relation
between inputs and output of the network.
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Massive parallelism
Brain computer as an information
or signal processing system, is
composed of a large number of a
simple processing elements, calledneurons. These neurons are
interconnected by numerous direct
links, which are called connection,
and cooperate which other to
perform a parallel distributed
processing (PDP) in order to soft a
desired computation tasks.
Connectionism
Brain computer is a highly
interconnected neurons system in
such a way that the state of one
neuron affects the potential of thelarge number of other neurons
which are connected according to
weights or strength. The key idea
of such principle isthe functional
capacity of biological neural nets
deters mostly not so of a single
neuron but of its connections
Associative
distributed memory
Storage of information in a brain is
supposed to be concentrated insynaptic connections of brain
neural network, or more precisely,
in the pattern of these connections
and strengths (weights) of the
synaptic connections.
A process of pattern
recognition and pattern
manipulation is based on:
How our brainmanipulates
with patterns ?
Principles of Brain Processing
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Biological
Neuron
- The simple
arithmeticcomputing
element
Biological Neuron
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Cell structures
Cell body
DendritesAxon
Synaptic terminals
Biological Neuron (2)
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synapses
axon dendrites
The information transmission happens at the synapses, i.e
Synaptic connection strengths among neurons are used to storethe acquired knowledge.
In a biological system, learning involves adjustments to the
synaptic connections between neurons
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Biological Neurons (3)
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Biological Neurons (4)
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1. Soma or body cell - is a large, round
central body in which almost all the
logical functions of the neuron are
realized (i.e. the processing unit).
2. The axon (output), is a nerve fibre
attached to the soma which can serve
as a final output channel of the
neuron. An axon is usually highly
branched.
3. The dendrites (inputs)- represent a
highly branching tree of fibers. These
long irregularly shaped nerve fibers
(processes) are attached to the soma
carrying electrical signals to the cell
4. Synapses are the point of contact
between the axon of one cell and the
dendrite of another, regulating a
chemical connection whose strength
affects the input to the cell.
The schematic
model of a
biological neuron
Synapses
Dendrites
Soma
AxonDendrite
from
other
Axon from
other
neuron
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Properties of ANNs
Learning from examples
labeled or unlabeled
Adaptivity
changing the connection strengths to learn things
Non-linearity
the non-linear activation functions are essential
Fault tolerance
if one of the neurons or connections is damaged, the
whole network still works quite well
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Part I:
1. Artificial Neural Networks (ANNs)
2. Artificial and human neurons (Biological Inspiration)
3. Tasks & Applications of ANNs
Part II:
1. Learning in Biological Systems
2. Learning with Artificial Neural Networks
Introduction to Artificial Neural Networks
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Applications of ANNsClassification
In marketing: consumer spending pattern classification
In defence: radar and sonar image classification
In agriculture & fishing: fruit, fish and catch grading
In medicine: ultrasound and electrocardiogram image classification, EEGs, medical diagnosis
Recognition and Identification
In general computing and telecommunications: speech, vision and handwriting recognition
In finance: signature verification and bank note verificationAssessment
In engineering: product inspection monitoring and control
In defence: target tracking
In security: motion detection, surveillance image analysis and fingerprint matching
Forecasting and Prediction
In finance: foreign exchange rate and stock market forecasting
In agriculture: crop yield forecasting , Deciding the category of potential food items
(e.g., edible or non-edible)
In marketing: sales forecasting
In meteorology: weather prediction
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Who are the Men of ANNs?!Computer scientists want to find out about the properties of non-symbolicinformation processing with neural nets and about learning systems ingeneral.
Statisticians use neural nets as flexible, nonlinear regression andclassification models.
Engineers of many kinds exploit the capabilities of neural networks in manyareas, such as signal processing and automatic control.
Cognitive scientists view neural networks as a possible apparatus to describemodels of thinking and consciousness (High-level brain function).
Neuro-physiologists use neural networks to describe and explore medium-level brain function (e.g. memory, sensory system, motorics).
Physicists use neural networks to model phenomena in statistical mechanics
and for a lot of other tasks.Biologists use Neural Networks to interpret nucleotide sequences.
Philosophers and some other people may also be interested in NeuralNetworks for various reasons
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Operation of Biological Neuron
The spikes travelling along the axon of the pre-synaptic neurontrigger the release of neurotransmitter substances at thesynapse.
The neurotransmitters cause excitation or inhibition in thedendrite of the post-synaptic neuron.
The integration of the excitatory and inhibitory signals mayproduce spikes in the post-synaptic neuron.
The contribution of the signals depends on the strength of thesynaptic connection.
25MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer
Excitation means positive product between the incoming
spike rate and the corresponding synaptic weight;
Inhibition means negative product between the incomingspike rate and the corresponding synaptic weight;
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Inputs
Output
An artificial neural network is composed of many
artificial neurons that are linked together accordingto a specific network architecture. The objective of
the neural network is to transform the inputs into
meaningful outputs.
ANN Architecture
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Neurons are arranged in layers. Neurons work by processing information. They
receive and provide information in form of spikes.
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The artificial neuron receives one or more inputs (representing the one or more
dendrites),
At each neuron, every input has an associated weight which modifies the
strength of each input and sums them together,
The sum of each neuron is passed through a function known as an activation
function ortransfer function in order to produce an output (representing a
biological neuron's axon)
ANN Architecture (2)
Inputs Output
http://en.wikipedia.org/wiki/Dendritehttp://en.wikipedia.org/wiki/Activation_functionhttp://en.wikipedia.org/wiki/Activation_functionhttp://en.wikipedia.org/wiki/Transfer_functionhttp://en.wikipedia.org/wiki/Axonhttp://en.wikipedia.org/wiki/Axonhttp://en.wikipedia.org/wiki/Transfer_functionhttp://en.wikipedia.org/wiki/Activation_functionhttp://en.wikipedia.org/wiki/Activation_functionhttp://en.wikipedia.org/wiki/Dendrite -
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Input
s
Outputw2
w1
w3
wn
wn-1
. . .
x1
x2
x3
xn-1
xn
y)(;
1
zHyxwzn
i
ii
Each neuron takes one or more inputs and produces an output. At eachneuron, every input has an associated weight which modifies the strength of
each input. The neuron simply adds together all the inputs and calculates an
output to be passed on.
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ANN Architecture (3)
f A
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Models of A Neuron
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M d l f A N (2)
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Axon
Terminal Branches
of AxonDendrites
S
x1
x2
w1
w2
wn
xn
x3 w3
Models of A Neuron (2)
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M d l f A N (3)
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1. A set of synapses, or connection link: each of whichis characterized by a weight or strength of its own wkj.
Specifically, a signal xj at the input synapse j connected toneuron k is multiplied by the synaptic wkj
2. An adder: For summing the input signals, weighted byrespective synaptic strengths of the neuron in a linear
operation.
3. Activation function: For limiting of the amplitude of theoutput of the neuron to limited range. The activation function
is referred to as a Squashing (i.e. limiting) function {interval
[0,1], or, alternatively [-1,1]}
Models of A Neuron (3)
Three elements:
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BiasThe bias has the effect of increasing or lowering the net input of
the activation function depending on whether it is +/-
yk= (vk) = (uk+ bk) = (S wkjxj + bk)An artificial neuron:
- computes the weighted sum of its input (called its net input)- adds its bias (the effect of applying affine transformation to the output vk)
- passes this value through an activation function
We say that the neuron fires (i.e. becomes active) if
its outputs is above zero.This extra free variable (bias) makes the neuron more
powerful.
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Activation Function (vk)It defines the output of the neuron given an input or set of inputs. A standard
computer chip circuit can be seen as a digital network of activation functions
that can be "ON" (1) or "OFF" (0), depending on input,
The best activation function is the non-linear function. Linear functions are
limited because the output is simply proportional to the input.
33MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer
Three basic types of activation function:1. Threshold function,
2. Linear function,
3. Sigmoid function.
A ti ti f ti (2)
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Threshold (Step) function
The outputykof this activation function is binary, depending onwhether the input meets a specified threshold. The "signal" is sent,
i.e. the output is set to one, if the activation meets the threshold.
Activation functions (2)
McColloch-Pitts ModelThreshold Logic Unit
(TLU),since 194334MTR607: Learning Algorithms and Neural Networks Dr. Alaa Sagheer
A ti ti f ti (3)
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Piecewise Linear Function- The amplification factor inside the linear region of operation is assumed to beunity.
- This form may be viewed as an approximation to a non linear amplifier
Activation functions (3)
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A ti ti f ti (4)
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Sigmoid function
Where a is the slope parameter ofthe sigmoid function
Activation functions (4)
- A fairly simple non-linear function, such as the logistic function.- As the slop parameter approaches infinity the sigmoid function becomes a
threshold function
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Artificial Neural Networks
Early ANN Models:
McCulloch-Pitts , Perceptron, ADALINE,
Hopfield Network,
Current Models:
Multilayer feed forward networks (Multilayer
perceptrons- Back propagation )
Radial Basis Function networks
Self Organizing Networks
...
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F db k
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FeedbackFeedback is a dynamic system whenever occurs in
almost every part of the nervous system,
Feedback is giving one or more closed path for
transmission of signals around the system,
It plays important role in study of special class of
neural networks known as Recurrent networks.
F db k (2)
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Feedback (2)
The system is assumed to be linear and has a forward path (A)and a feedback path (B),
The output of the forward channel determines its own output
through the feedback channel.
F db k (3)
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Feedback (3)
E.g. considerA is a fixed weight and B is a unit delay operatorz-1 .
F db k (4)
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Feedback (4)
Then, we may expressyk(n) as an infinite weighted summation of
present and past samples of the input signalxj(n).
Therefore, feedback systems are controlled by weight.
F db k (5)
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Feedback (5)Feedback systems are controlled
by weight.
1. For positive weight, we have
stable systems, i,e, convergent
output y,
2. For negative weight, we have,
unstable systems, i.e divergent
output y.. (Linear andExponential)
N t k A hit t
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Network ArchitecturesThree different classes of network architectures:
1. Single-layer feed forward networks,
2. Multilayer feed forward networks,
3. Recurrent networks.
Si l l f d f d t k
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Single-layer feed forward network
- Input layer of source nodes that projects directly
onto an output layer of neurons.
- Single-layer referring to the output layer ofcomputation nodes (neuron).
M ltil f d f d t k
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Multilayer feed forward networkIt contains one or more hidden
layers (hidden neurons).Hidden refers to the part ofthe neural network is not seen
directly from either input or
output of the network .The function of hidden neuron
is to intervene between input
and output.
By adding one or more hiddenlayers, the network is able to
extract higher-order statistics
from input
R t N t k
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Recurrent NetworksIt is different from feed
forward neural network in thatit has at least one feedback
loop.
Recurrent network may consist
of single layer of neuron witheach neuron feeding its output
signal back to the inputs of all
the other neurons.Note: There
are no self-feedback.
Feedback loops have a
profound impact on learning
and overall performance.
H t D id N t k T l ?
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Slide 47
What transfer function should be used?
How many inputs does the network need?
How many hidden layers does the network need?
How many hidden neurons per hidden layer?
How many outputs should the network have?
There is no standard methodology to determinate these values.
Even there is some heuristic points, final values are
determinate by a trial and error procedure.
How to Decide on a Network Topology?
K l d R t ti
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Knowledge Representation
The main characteristic of knowledge representation has two
folds:
1) What information is actually made explicit?
2) How the information is physically encoded for subsequent use?
Knowledgeis referred to the stored information or models used
by a person or machine to interpret, predict and, appropriately,respond to the outside.
A good solution depends on a good representation of
knowledge
K l d R t ti (2)
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There are two kinds of Knowledge:1) The known world states, or facts, (prior knowledge),
2) Observations (measurements) of the world, obtained by
sensors to probe the environment.
Knowledge Representation (2)
These observations
represent the pool of
information, from
which examples areused to train the NN
K l d R t ti (3)
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These Examples can be labeled or unlabeled
In labeled examples
Each example representing an input signal is paired with a
corresponding desired response,
Labeled examples may be expensive to collect, as they requireavailability of a teacher to provide a desired response foreach labeled example.
Un labeled examples
Unlabeled examples are usually abundant as there is no needfor supervision.
Knowledge Representation (3)
K l d R t ti (3)
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Design of neural network may proceed as follow:An appropriate architecture for the neural network, with aninput layer consisting of source nodes equal in number to the
pixels of an input image.
The recognition performance of trained network is tested with
data not seen before (testing).
This phase of the network design called learning
Knowledge Representation (3)
R l f K l d R t ti
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Roles of Knowledge RepresentationThere are four rules for knowledge representation:
Rule 1:
Similar inputs (i.e., patterns) drawn from similar
classes should usually produce similar representation
inside the network, and should therefore be classified as
belonging to the same class.
There are plethora (many) of measures for
determining the similarity between inputs
R l f K l d R t ti (2)
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(1)
Roles of Knowledge Representation (2)A commonly used measure of similarity is the Euclidian Distance
Let xi denotes an m-by-1 vector
R l f K l d R t ti (3)
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Roles of Knowledge Representation (3)Another measure is the dot productorinner product com
Given a pair of vectors xi andxj of the same dimension, their innerproduct will be (the projection of vectorx
ionto vectorx
j)
Please note that:
R l f K l d R t ti (4)
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Using Eq.(1) to write
Roles of Knowledge Representation (4)The smaller the Euclidean distance x
i - x
j(i.e. the more similar
the vectorxi andxj are), the larger the inner product xiT
xj will be.To formalize this relationship, we normalizethe vectors x
i andx
jto have a unit length, i.e.:
The minimization of the Euclidean distance d(xi, xj ) corresponds
to maximization of the inner product (xi, x
j )..and, therefore, the
similarity between the vectors xi and xj
R l f K l d R t ti (5)
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If the vectors xi and x
jare stochastic (drown from different
population of data)
Roles of Knowledge Representation (5)
Where C-1 is the inverse of the covariance
matrix C. It is supposed that the
covariance matrix is the same for both
For a prescribed C, the smaller the distance d is themore similar the vectors xi and xj will be
R l f K l d R t ti (6)
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Rule 2:
Item to be categorized as separate classes should be given widelydifferent representation in work.
Rule 3:If a particular feature is important, then there should be large
number of neurons involved in the representation of that item inthe network.
Rule 4:Prior information and invariance should be built into the design of
a neural network when ever they are available, so as to simplifythe network design by its not having to learn them.
Roles of Knowledge Representation (6)
Rule 4 is particularly important and highly desirable
Roles of Kno ledge Representation (7)
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1) Biological visual and auditory networks are very specialized,
2) NN with SS has a smaller number of free parameters available for
adjustment than other networks. Then, they need a small training dataset,
learns faster and generalize better.
3) Rate of information transmission through a specialized network is faster,
4) Cost of building a specialized network is minimum, due to small size.
Roles of Knowledge Representation (7)Rule 4 is particularly important and highly desirable
because it results in an NN with a Specialized Structure (SS)
How to build prior information into NN design?
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How to build prior information into NN design?There are currently no well-defined rules for doing this; but wehave some procedure are known to yield useful rules. In particular,we may use a combination of two techniques:
1. Restricting the network architecture (using local connections)
2. Constraining the choice of synaptic weight (using the weightsharing)
The latter tech is so
important because it
leads to reducing
significantly freeparameters
How to build invariance into NNs design?
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How to build invariance into NNs design?
There are three technique for rendering classifier-type NNs
invariant to transformations:
1. Invariance by structure.
2. Invariance by training.
3. Invariance by feature space
Consider any of the following:
1) When an object rotates, the perceived image, by observer, will change as well,
2) The utterance of a spoken person may be soft or loud..slower or quicker,
3) ..
A classifier should be invariant to different transformation
Or
A class estimate represented by an output of the classifierMUST not be affected by transformations of the observed
signal applied to the classifier input
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Learning in
Biological Systems
Learning in Biological Systems
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Learning in Biological Systems
Learning approach based on modeling adaptation in
biological neural systems
Learning = learning by adaptation
The young animal learns that the green fruits are sour,
while the yellowish/reddish ones are sweet. The
learning happens by adapting the fruit picking
behaviour
Learning in Biological Systems (2)
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From experience: examples / training data
Learning happens by changing of the synaptic
strengths,
Synapses change size and strength with experience (or
examples or training data),
Strength of connection between the neurons is stored
as a weight-value for the specific connection,
Learning the solution to a problem = changing the
connection weights
Learning in Biological Systems (2)
Learning in Biological Systems (3)
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Hebbian Learning
When two connected neurons are firing at the same
time, the strength of the synapse between them
increases,
Neurons that fire together, wire together
Learning in Biological Systems (3)
Learning in ANN
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Learning in ANNWe may categorize the learning process through Neural
Networks function as follows:
1. Learning with a teacher,
- Supervised Learning
2. Learning without a teacher,
- Unsupervised Learning
- Reinforcement Learning
Supervised Learning
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In supervised learning, both the
inputs and the outputs are
provided. The network then
processes the inputs and compares
its resulting outputs against the
desired outputs
Errors are then calculated, causingthe system to adjust the weights
which control the network. This
process occurs over and over as the
weights are continually improved.
Supervised Learning
Supervised learning process
constitutes a closed-loop
feedback system but unknown
environment is outside the loop,
Supervised Learning (2)
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It is based on a labeled
training set.
The class of each piece
of data in training set is
known.
Class labels are pre-
determined and
provided in the trainingphase.
A
B
A
BA
B
Class
Class
Class
Class
Class
Class
Supervised Learning (2)
Understanding Supervised Learning
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g p g
A
BA
B A
B
Two Possible Solutions
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Two Possible Solutions
A
B
A
B
A
B
A
B A
B
A
B
How to solve a given problem of supervised learning?
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How to solve a given problem of supervised learning?Various steps have to be considered:
1. Determine the type of training examples,
2. Gather a training data set that satisfactory describe the given problem,
3. After the training process we can test the performance of learned artificial
neural network with the test (validation) data set,
4. Test data set consist of data that has not been introduced to artificial
neural network while learning.
Reinforcement Learning
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Reinforcement Learning
The learning of inputoutputmapping is performed through
continued interaction with the
environment in order to minimize
a scalar index of performance.
Or
A machine learning technique
that sets parameters of an
artificial neural network, where
data is usually not given, but
generated by interactions with the
environment.
Reinforcement Learning (2)
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Reinforcement Learning (2)
Reinforcement learning is built around critic that converts primary
reinforcement signal received from the environment into a higher
quality reinforcement signal
Unsupervised Learning
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U supe v sed e g
No help from the outside,
No information available on the desired output,
Input: set of patterns P, from n-dimensional space S, but little /no information about their classification, evaluation, interestingfeatures, etc.
It must learn these by itself!
Learning by doingTasks: Used to pick out structure in the input
Clustering - Group patterns based on similarity,
Vector Quantization - Fully divide up S into a small set ofregions (defined by codebook vectors) that also helps cluster P,
Feature Extraction - Reduce dimensionality of S by removingunimportant features (i.e. those that do not help in clustering P)
Supervised vs. Unsupervised
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Supervised vs. Unsupervised
Task performed
Classification
Pattern Recognition
NN model
Preceptron,Feed-Forward NN
Task performed
Clustering, Pattern Recognition
Feature Extraction, VQ
NN Model
Self Organizing Maps,ART