This document provides an overview of artificial neural networks (ANNs). It discusses how ANNs are inspired by biological neural networks and are composed of interconnected nodes that mimic neurons. ANNs use a learning process to update synaptic connection weights between nodes based on training data to perform tasks like pattern recognition. The document outlines the history of ANNs and covers popular applications. It also describes common ANN properties, architectures, and the backpropagation algorithm used for training multilayer networks.
Artificial Neural Networks ppt.pptx for final sem cseNaveenBhajantri1
This document provides an overview of artificial neural networks. It discusses the biological inspiration from neurons in the brain and how artificial neural networks mimic this structure. The key components of artificial neurons and various network architectures are described, including fully connected, layered, feedforward, and modular networks. Supervised and unsupervised learning approaches are covered, with backpropagation highlighted as a commonly used supervised algorithm. Applications of neural networks are mentioned in areas like medicine, business, marketing and credit evaluation. Advantages include the ability to handle complex nonlinear problems and noisy data.
Chapter 9 of the document discusses advanced classification methods including Bayesian belief networks, classification using backpropagation neural networks, support vector machines, classification with frequent patterns, lazy learning, and other techniques. It describes how these methods work, including how Bayesian networks are constructed, how backpropagation trains neural networks, how support vector machines find optimal separating hyperplanes, and considerations around efficiency and interpretability. The chapter also covers mathematical mappings of classification problems and discriminative versus generative classifiers.
Chapter 9 of the book discusses advanced classification methods including Bayesian belief networks, classification using backpropagation neural networks, support vector machines, frequent pattern-based classification, lazy learning, and other techniques. It describes how these methods work, including how to construct Bayesian networks, train neural networks using backpropagation, find optimal separating hyperplanes with support vector machines, and more. The chapter also covers topics like network topologies, training scenarios, efficiency and interpretability of different methods.
This chapter discusses advanced classification methods, including Bayesian belief networks, classification using backpropagation neural networks, support vector machines (SVM), and lazy learners. It describes Bayesian belief networks as probabilistic graphical models that represent conditional dependencies between variables. Backpropagation neural networks are introduced as a way to perform nonlinear regression to approximate functions through adjusting weights in a multi-layer feedforward network. SVM is covered as a method that transforms data into a higher dimensional space to find an optimal separating hyperplane, using support vectors.
This document provides an introduction to artificial neural networks. It discusses biological neurons and how artificial neurons are modeled. The key components of a neural network including the network architecture, learning approaches, and the backpropagation algorithm for supervised learning are described. Applications and advantages of neural networks are also mentioned. Neural networks are modeled after the human brain and learn by modifying connection weights between nodes based on examples.
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
X-TREPAN : A Multi Class Regression and Adapted Extraction of Comprehensible ...csandit
The document describes an algorithm called X-TREPAN that extracts decision trees from trained neural networks. X-TREPAN is an enhancement of the TREPAN algorithm that allows it to handle both multi-class classification and multi-class regression problems. It can also analyze generalized feed forward networks. The algorithm was tested on several real-world datasets and was found to generate decision trees with good classification accuracy while also maintaining comprehensibility.
X-TREPAN: A MULTI CLASS REGRESSION AND ADAPTED EXTRACTION OF COMPREHENSIBLE D...cscpconf
In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees from neural networks. We empirically evaluated the performance of the algorithm on a set of databases from real world events. This benchmark enhancement was achieved by adapting Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The models are then compared with X-TREPAN for comprehensibility and classification accuracy. Furthermore, we validate the experimentations by applying statistical methods. Finally, the modified algorithm is extended to work with multi-class regression problems and the ability to comprehend generalized feed forward networks is achieved.
NEURAL NETWORK IN MACHINE LEARNING FOR STUDENTShemasubbu08
- Artificial neural networks are computational models inspired by the human brain that use algorithms to mimic brain functions. They are made up of simple processing units (neurons) connected in a massively parallel distributed system. Knowledge is acquired through a learning process that adjusts synaptic connection strengths.
- Neural networks can be used for pattern recognition, function approximation, and associative memory in domains like speech recognition, image classification, and financial prediction. They have flexible inputs, resistant to errors, and fast evaluation, though interpretation is difficult.
The document discusses different types of machine learning paradigms including supervised learning, unsupervised learning, and reinforcement learning. It then provides details on artificial neural networks, describing them as consisting of simple processing units that communicate through weighted connections, similar to neurons in the human brain. The document outlines key aspects of artificial neural networks like processing units, connections between units, propagation rules, and learning methods.
Artificial neural networks are computer programs that can recognize patterns in data and produce models to represent that data. They are inspired by the human brain in how knowledge is acquired through learning and stored in the connections between neurons. Neural networks learn by adjusting the strengths of connections between neurons based on examples provided during training. They are able to model and learn both linear and nonlinear relationships in data.
An Artificial Neural Network (ANN) is a computational model inspired by the structure and functioning of the human brain's neural networks. It consists of interconnected nodes, often referred to as neurons or units, organized in layers. These layers typically include an input layer, one or more hidden layers, and an output layer.
This document provides an overview of autoencoders and their use in unsupervised learning for deep neural networks. It discusses the history and development of neural networks, including early work in the 1940s-1980s and more recent advances in deep learning. It then explains how autoencoders work by setting the target values equal to the inputs, describes variants like denoising autoencoders, and how stacking autoencoders can create deep architectures for tasks like document retrieval, facial recognition, and signal denoising.
The document discusses adaptive channel equalization using neural networks. It provides an overview of neural networks and their application to channel equalization. Specifically, it summarizes various neural network architectures that have been used for equalization, including multilayer perceptrons, functional link artificial neural networks, Chebyshev neural networks, and radial basis function networks. It compares the bit error rate performance of these different neural network equalizers with traditional linear equalizers such as LMS and RLS. Overall, the document finds that neural network equalizers can better handle nonlinear channel distortions compared to linear equalizers and that radial basis function networks provide particularly good performance for channel equalization applications.
1. A perceptron is a basic artificial neural network that can learn linearly separable patterns. It takes weighted inputs, applies an activation function, and outputs a single binary value.
2. Multilayer perceptrons can learn non-linear patterns by using multiple layers of perceptrons with weighted connections between them. They were developed to overcome limitations of single-layer perceptrons.
3. Perceptrons are trained using an error-correction learning rule called the delta rule or the least mean squares algorithm. Weights are adjusted to minimize the error between the actual and target outputs.
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Neural networks and deep learning are machine learning techniques inspired by the human brain. Neural networks consist of interconnected nodes that process input data and pass signals to other nodes. The main types discussed are artificial neural networks (ANNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). ANNs can learn nonlinear relationships between inputs and outputs. CNNs are effective for image processing by learning relevant spatial features. RNNs capture sequential dependencies in data like text. Deep learning uses neural networks with many layers to learn complex patterns in large datasets.
This document provides an overview of neural networks. It discusses how the human brain works and how artificial neural networks are modeled after the human brain. The key components of a neural network are neurons which are connected and can be trained. Neural networks can perform tasks like pattern recognition through a learning process that adjusts the connections between neurons. The document outlines different types of neural network architectures and training methods, such as backpropagation, to configure neural networks for specific applications.
Web Spam Classification Using Supervised Artificial Neural Network Algorithmsaciijournal
Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are
more efficient, generic and highly adaptive. Neural Network based technologies have high ability of
adaption as well as generalization. As per our knowledge, very little work has been done in this field using
neural network. We present this paper to fill this gap. This paper evaluates performance of three supervised
learning algorithms of artificial neural network by creating classifiers for the complex problem of latest
web spam pattern classification. These algorithms are Conjugate Gradient algorithm, Resilient Backpropagation learning, and Levenberg-Marquardt algorithm.
Open Source and AI - ByWater Closing Keynote Presentation.pdfJessica Zairo
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How To Update One2many Field From OnChange of Field in Odoo 17Celine George
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How to Manage Early Receipt Printing in Odoo 17 POSCeline George
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View Inheritance in Odoo 17 - Odoo 17 SlidesCeline George
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2. Artificial Neural Networks
Computational models inspired by the human
brain:
Algorithms that try to mimic the brain.
Massively parallel, distributed system, made up of
simple processing units (neurons)
Synaptic connection strengths among neurons are
used to store the acquired knowledge.
Knowledge is acquired by the network from its
environment through a learning process
3. History
late-1800's - Neural Networks appear as an
analogy to biological systems
1960's and 70's – Simple neural networks appear
Fall out of favor because the perceptron is not
effective by itself, and there were no good algorithms
for multilayer nets
1986 – Backpropagation algorithm appears
Neural Networks have a resurgence in popularity
More computationally expensive
4. Applications of ANNs
ANNs have been widely used in various domains
for:
Pattern recognition
Function approximation
Associative memory
5. Properties
Inputs are flexible
any real values
Highly correlated or independent
Target function may be discrete-valued, real-valued, or
vectors of discrete or real values
Outputs are real numbers between 0 and 1
Resistant to errors in the training data
Long training time
Fast evaluation
The function produced can be difficult for humans to
interpret
6. When to consider neural networks
Input is high-dimensional discrete or raw-valued
Output is discrete or real-valued
Output is a vector of values
Possibly noisy data
Form of target function is unknown
Human readability of the result is not important
Examples:
Speech phoneme recognition
Image classification
Financial prediction
7. November 14, 2022 Data Mining: Concepts and Techniques 7
A Neuron (= a perceptron)
The n-dimensional input vector x is mapped into variable y by
means of the scalar product and a nonlinear function mapping
t
-
f
weighted
sum
Input
vector x
output y
Activation
function
weight
vector w
w0
w1
wn
x0
x1
xn
)
sign(
y
e
For Exampl
n
0
i
t
x
w i
i
8. Perceptron
Basic unit in a neural network
Linear separator
Parts
N inputs, x1 ... xn
Weights for each input, w1 ... wn
A bias input x0 (constant) and associated weight w0
Weighted sum of inputs, y = w0x0 + w1x1 + ... + wnxn
A threshold function or activation function,
i.e 1 if y > t, -1 if y <= t
9. Artificial Neural Networks (ANN)
Model is an assembly of
inter-connected nodes
and weighted links
Output node sums up
each of its input value
according to the weights
of its links
Compare output node
against some threshold t
X1
X2
X3
Y
Black box
w1
t
Output
node
Input
nodes
w2
w3
)
( t
x
w
I
Y
i
i
i
Perceptron Model
)
( t
x
w
sign
Y
i
i
i
or
10. Types of connectivity
Feedforward networks
These compute a series of
transformations
Typically, the first layer is the input
and the last layer is the output.
Recurrent networks
These have directed cycles in their
connection graph. They can have
complicated dynamics.
More biologically realistic.
hidden units
output units
input units
11. Different Network Topologies
Single layer feed-forward networks
Input layer projecting into the output layer
Input Output
layer layer
Single layer
network
12. Different Network Topologies
Multi-layer feed-forward networks
One or more hidden layers. Input projects only from
previous layers onto a layer.
Input Hidden Output
layer layer layer
2-layer or
1-hidden layer
fully connected
network
14. Different Network Topologies
Recurrent networks
A network with feedback, where some of its inputs
are connected to some of its outputs (discrete time).
Input Output
layer layer
Recurrent
network
15. Algorithm for learning ANN
Initialize the weights (w0, w1, …, wk)
Adjust the weights in such a way that the output
of ANN is consistent with class labels of training
examples
Error function:
Find the weights wi’s that minimize the above error
function
e.g., gradient descent, backpropagation algorithm
2
)
,
(
i
i
i
i X
w
f
Y
E
16. Optimizing concave/convex function
Maximum of a concave function = minimum of a
convex function
Gradient ascent (concave) / Gradient descent (convex)
Gradient ascent rule
24. Decision surface of a perceptron
Decision surface is a hyperplane
Can capture linearly separable classes
Non-linearly separable
Use a network of them
27. Multi-layer Networks
Linear units inappropriate
No more expressive than a single layer
„Introduce non-linearity
Threshold not differentiable
„Use sigmoid function
31. November 14, 2022 Data Mining: Concepts and Techniques 31
Backpropagation
Iteratively process a set of training tuples & compare the network's
prediction with the actual known target value
For each training tuple, the weights are modified to minimize the mean
squared error between the network's prediction and the actual target
value
Modifications are made in the “backwards” direction: from the output
layer, through each hidden layer down to the first hidden layer, hence
“backpropagation”
Steps
Initialize weights (to small random #s) and biases in the network
Propagate the inputs forward (by applying activation function)
Backpropagate the error (by updating weights and biases)
Terminating condition (when error is very small, etc.)
33. November 14, 2022 Data Mining: Concepts and Techniques 33
How A Multi-Layer Neural Network Works?
The inputs to the network correspond to the attributes measured for
each training tuple
Inputs are fed simultaneously into the units making up the input layer
They are then weighted and fed simultaneously to a hidden layer
The number of hidden layers is arbitrary, although usually only one
The weighted outputs of the last hidden layer are input to units making
up the output layer, which emits the network's prediction
The network is feed-forward in that none of the weights cycles back to
an input unit or to an output unit of a previous layer
From a statistical point of view, networks perform nonlinear regression:
Given enough hidden units and enough training samples, they can
closely approximate any function
34. November 14, 2022 Data Mining: Concepts and Techniques 34
Defining a Network Topology
First decide the network topology: # of units in the input
layer, # of hidden layers (if > 1), # of units in each hidden
layer, and # of units in the output layer
Normalizing the input values for each attribute measured in
the training tuples to [0.0—1.0]
One input unit per domain value, each initialized to 0
Output, if for classification and more than two classes, one
output unit per class is used
Once a network has been trained and its accuracy is
unacceptable, repeat the training process with a different
network topology or a different set of initial weights
35. November 14, 2022 Data Mining: Concepts and Techniques 35
Backpropagation and Interpretability
Efficiency of backpropagation: Each epoch (one interation through the
training set) takes O(|D| * w), with |D| tuples and w weights, but # of
epochs can be exponential to n, the number of inputs, in the worst case
Rule extraction from networks: network pruning
Simplify the network structure by removing weighted links that have the
least effect on the trained network
Then perform link, unit, or activation value clustering
The set of input and activation values are studied to derive rules
describing the relationship between the input and hidden unit layers
Sensitivity analysis: assess the impact that a given input variable has on a
network output. The knowledge gained from this analysis can be
represented in rules
36. November 14, 2022 Data Mining: Concepts and Techniques 36
Neural Network as a Classifier
Weakness
Long training time
Require a number of parameters typically best determined empirically,
e.g., the network topology or “structure.”
Poor interpretability: Difficult to interpret the symbolic meaning behind
the learned weights and of “hidden units” in the network
Strength
High tolerance to noisy data
Ability to classify untrained patterns
Well-suited for continuous-valued inputs and outputs
Successful on a wide array of real-world data
Algorithms are inherently parallel
Techniques have recently been developed for the extraction of rules from
trained neural networks
38. Artificial Neural Networks (ANN)
X1 X2 X3 Y
1 0 0 0
1 0 1 1
1 1 0 1
1 1 1 1
0 0 1 0
0 1 0 0
0 1 1 1
0 0 0 0
X1
X2
X3
Y
Black box
0.3
0.3
0.3 t=0.4
Output
node
Input
nodes
otherwise
0
true
is
if
1
)
(
where
)
0
4
.
0
3
.
0
3
.
0
3
.
0
( 3
2
1
z
z
I
X
X
X
I
Y
40. November 14, 2022 Data Mining: Concepts and Techniques 40
A Multi-Layer Feed-Forward Neural Network
Output layer
Input layer
Hidden layer
Output vector
Input vector: X
wij
ij
k
i
i
k
j
k
j x
y
y
w
w )
ˆ
( )
(
)
(
)
1
(
41. General Structure of ANN
Activation
function
g(Si )
Si
Oi
I1
I2
I3
wi1
wi2
wi3
Oi
Neuron i
Input Output
threshold, t
Input
Layer
Hidden
Layer
Output
Layer
x1 x2 x3 x4 x5
y
Training ANN means learning
the weights of the neurons