neural networks

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Introduction 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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Main text books: “Neural Networks: A Comprehensive Foundation”, S. Haykin (very good -

theoretical) “Pattern Recognition with Neural Networks”, C. Bishop (very good-more

accessible) “Neural Network Design” by Hagan, Demuth and Beale (introductory)

Books emphasizing the practical aspects: “Neural Smithing”, Reeds and Marks “Practical 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

ASP Magazine, 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”’ IEEE Computer, March 1996’ p. 31-44.

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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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Digital Computers Analyze the problem to be

solved

Deductive Reasoning. We apply known rules to input data to produce output.

Computation is centralized, synchronous, and serial.

Not fault tolerant. One transistor goes and it no longer works.

Static connectivity.

Applicable if well defined rules with precise input data.

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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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:

ANN’s design is what distinguishes neural networks from other mathematical techniques

ANN is a network of many simple processors ("units“ or “neurons”), each unit has a small amount of local memory.

The units are connected by unidirectional communication channels ("connections"), which carry 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 neural networks.

Most neural networks have some kind of "training" rule whereby the weights of connections are adjusted on the basis of presented patterns.

In other words, neural networks "learn" from examples, just like children…and exhibit some structural capability for generalization.


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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 to improve 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 a problem.

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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What can a ANN do?Compute a known functionApproximate an unknown functionPattern RecognitionSignal Processing

…………..Learn to do any of the above

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Part I:

1. Artificial Neural Networks (ANNs)



Part II:




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Animals are able to react adaptively to changes in their external and internal environment, and they use their nervous system to perform these behaviours.

An appropriate model/simulation of the nervous system should be able to produce similar responses and behaviours in artificial systems.

The nervous system is build by relatively simple units, the neurons, so copying their behaviour and functionality should be the 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

BrainThe 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, 10000 others. In total 60 trillion synapses of connections.The brain is a highly complex, nonlinear and parallel computer (information-processing system)

An artificial neural network (ANN) is

a massively parallel distributed processor that has a natural

propensity for storing experimental 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 the adapting 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, called neurons. 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 the large number of other neurons

which are connected according to weights or strength. The key idea of such principle is the functional capacity of biological neural nets deters mostly not so of a single neuron but of its connections

Associative distributed memoryStorage of information in a brain is supposed to be concentrated in synaptic 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 brain manipulates

with patterns ?

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Biological Neuron

- The simple “arithmetic computing”

element

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Cell structuresCell bodyDendritesAxonSynaptic terminals

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synapses

axon dendrites

The information transmission happens at the synapses, i.e

Synaptic connection strengths among neurons are used to store the acquired knowledge.

In a biological system, learning involves adjustments to the synaptic connections between neurons

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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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Learning from examples labeled or unlabeled

Adaptivitychanging the connection strengths to learn

things

Non-linearitythe 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)



Part II:




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Classification 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 verification

Assessment 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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Computer scientists want to find out about the properties of non-symbolic information processing with neural nets and about learning systems in general.

Statisticians use neural nets as flexible, nonlinear regression and classification models.

Engineers of many kinds exploit the capabilities of neural networks in many areas, such as signal processing and automatic control.

Cognitive scientists view neural networks as a possible apparatus to describe models 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

Neural Networks for various reasons

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The spikes travelling along the axon of the pre-synaptic neuron trigger the release of neurotransmitter substances at the synapse.The neurotransmitters cause excitation or inhibition in the dendrite of the post-synaptic neuron.The integration of the excitatory and inhibitory signals may produce spikes in the post-synaptic neuron. The contribution of the signals depends on the strength of the synaptic connection.• Excitation means positive product between the incoming

spike rate and the corresponding synaptic weight;

• Inhibition means negative product between the incoming spike 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 according to a specific network architecture. The objective of the neural network is to transform the inputs into

meaningful outputs.

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Neurons are arranged in layers. Neurons work by processing information. They receive and provide information in form of spikes.

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 or transfer function in order to produce an output (representing a biological neuron's axon)

Inputs Output

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Inputs

Outputw2

w1

w3

wn

wn-1

. . .

x1

x2

x3

…

xn-1

xn

y)(;

1

zHyxwzn

iii

Each neuron takes one or more inputs and produces an output. At each neuron, 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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Axon

Terminal Branches of Axon

Dendrites

S

x1

x2

w1

w2

wnxn

x3 w3

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1. A set of synapses, or connection link: each of which is characterized by a weight or strength of its own wkj. Specifically, a signal xj at the input synapse ‘j’ connected to neuron ‘k’ is multiplied by the synaptic wkj

2. An adder: For summing the input signals, weighted by respective synaptic strengths of the neuron in a linear operation.

3. Activation function: For limiting of the amplitude of the output 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]}

Three elements:

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The bias has the effect of increasing or lowering the net input of the activation function depending on whether it is +/-

yk = Ø(vk) = Ø(uk + bk) = Ø(Swkjxj + 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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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.

Three basic types of activation function:

1. Threshold function,2. Linear function,3. Sigmoid function.

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Activation functions (2)

McColloch-Pitts Model

Threshold Logic Unit (TLU), since 1943

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Where “a” is the slope parameter of

the sigmoid function


- 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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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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Feedback 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.

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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.

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E.g. consider A is a fixed weight and B is a unit delay operator z-1 .

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Then, we may express yk(n) as an infinite weighted summation of present and past samples of the input signal xj(n).

Therefore, feedback systems are controlled by weight.

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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 and Exponential)

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Three different classes of network architectures:

1. Single-layer feed forward networks,2. Multilayer feed forward networks,3. Recurrent networks.

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- Input layer of source nodes that projects directly onto an output layer of neurons.- “Single-layer” referring to the output layer of computation nodes (neuron).

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It contains one or more hidden layers (hidden neurons).“Hidden” refers to the part of the 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 hidden layers, the network is able to extract higher-order statistics from input

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It is different from feed forward neural network in that it has at least one feedback loop. Recurrent network may consist of single layer of neuron with each 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.

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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.

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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?

KnowledgeKnowledge is 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

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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.

These observations represent the pool of information, from which examples are used to train the NN

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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 require availability of a “teacher” to provide a desired response for each labeled example.

Un labeled examplesUnlabeled examples are usually abundant as there is no need for supervision.

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Design of neural network may proceed as follow:An appropriate architecture for the neural network, with an input 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

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There are four rules for knowledge representation:

Rule 1: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

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(1)

A commonly used measure of similarity is the Euclidian DistanceEuclidian Distance

Let xi denotes an m-by-1 vector

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Another measure is the dot product dot product or inner product inner product com

Given a pair of vectors xi and xj of the same dimension, their inner product will be (the projection of vector xi onto vector xj)

Please note that:

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Using Eq.(1) to write

The smaller the Euclidean distance ║x i - xj ║(i.e. the more

similar the vector xi and xj are), the larger the inner product xi

T xj will be.To formalize this relationship, we normalize

the vectors x i and xj to have a unit length,

i.e.:

The minimization of the Euclidean distance d(x i , xj )

corresponds to maximization of the inner product (x i , xj )..and,

therefore, the similarity between the vectors x i and xj

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If the vectors x i and xj are stochastic (drown

from different population of data)

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 the more similar the vectors xi and xj

will be

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Rule 2:Rule 2:Item to be categorized as separate classes should be given widely different representation in work.

Rule 3: Rule 3: If a particular feature is important, then there should be large number of neurons involved in the representation of that item in the network.

Rule 4: Rule 4: Prior information and invariance should be built into the design of a neural network when ever they are available, so as to simplify the network design by its not having to learn them.

Rule 4 is particularly important and highly desirable

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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.

Rule 4 is particularly important and highly desirable because it results in an NN with a

Specialized Structure (SS)

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There are currently no well-defined rules for doing this; but we have 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 weight sharing)

The latter tech is so important because it leads

to reducing significantly

free parameters

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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 classifier MUST not be affected by transformations of the observed signal

applied to the classifier input

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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

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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

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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”

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We 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

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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, causing the 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, 04/12/23

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 training phase.

A

BA

BA

B

Class

Class

Class

Class

Class

Class

Supervised

Learning (2)

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A

BA

B A

B

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A

B

A

B

A

B

A

B A

B

A

B

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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.

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The learning of input –output mapping 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.

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Reinforcement learning is built around critic that converts primary reinforcement signal received from the environment

into a higher quality reinforcement signal

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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, interesting features, etc.

It must learn these by itself! Learning by doing

Tasks: Used to pick out structure in the inputClustering - Group patterns based on similarity,Vector Quantization - Fully divide up S into a small

set of regions (defined by codebook vectors) that also helps cluster P,

Feature Extraction - Reduce dimensionality of S by removing unimportant features (i.e. those that do not help in clustering P)

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Task performedClassificationPattern Recognition

NN model Preceptron,Feed-Forward NN

Task performedClustering, Pattern Recognition

Feature Extraction, VQ NN Model

Self Organizing Maps, ART

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neural networks

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