Predicting HDB Resale Prices - Conducting Linear Regression Analysis With Orange
SOFM based calssification for LU
1. Outline
Self-Organized Map
Case Study
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Self-Organized Map
Mr.Sanjay Shitole
Department of Information Technology
Usha Mittal Institute of Technology for Women
SNDT Women’s University, Santacruz(w), Mumbai.
27 March 2010
Mr.Sanjay Shitole Self-Organized Map
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Outline of Topics
1 Self-Organized Map
Applications
Architectures
Algorithm
2 Case Study
Land-use Classification
Classification of Antarctic Satellite Imagery
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Mr.Sanjay Shitole Self-Organized Map
3. Outline
Self-Organized Map
Case Study
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Applications
Architectures
Algorithm
Applications
Statistical pattern recognition, especially recognition of
speech.
Mr.Sanjay Shitole Self-Organized Map
4. Outline
Self-Organized Map
Case Study
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Applications
Architectures
Algorithm
Applications
Statistical pattern recognition, especially recognition of
speech.
Control of robot arms, and other problems in robotics.
Mr.Sanjay Shitole Self-Organized Map
5. Outline
Self-Organized Map
Case Study
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Applications
Architectures
Algorithm
Applications
Statistical pattern recognition, especially recognition of
speech.
Control of robot arms, and other problems in robotics.
Control of industrial process, especially diffusion processes in
the production of semiconductor substrates.
Mr.Sanjay Shitole Self-Organized Map
6. Outline
Self-Organized Map
Case Study
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Applications
Architectures
Algorithm
Applications
Statistical pattern recognition, especially recognition of
speech.
Control of robot arms, and other problems in robotics.
Control of industrial process, especially diffusion processes in
the production of semiconductor substrates.
Automatic synthesis of digital systems.
Mr.Sanjay Shitole Self-Organized Map
7. Outline
Self-Organized Map
Case Study
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Applications
Architectures
Algorithm
Applications
Statistical pattern recognition, especially recognition of
speech.
Control of robot arms, and other problems in robotics.
Control of industrial process, especially diffusion processes in
the production of semiconductor substrates.
Automatic synthesis of digital systems.
Adaptive devices for various telecommunications tasks.
Mr.Sanjay Shitole Self-Organized Map
8. Outline
Self-Organized Map
Case Study
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Applications
Architectures
Algorithm
Applications
Statistical pattern recognition, especially recognition of
speech.
Control of robot arms, and other problems in robotics.
Control of industrial process, especially diffusion processes in
the production of semiconductor substrates.
Automatic synthesis of digital systems.
Adaptive devices for various telecommunications tasks.
Image compression.
Mr.Sanjay Shitole Self-Organized Map
9. Outline
Self-Organized Map
Case Study
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Applications
Architectures
Algorithm
Applications
Statistical pattern recognition, especially recognition of
speech.
Control of robot arms, and other problems in robotics.
Control of industrial process, especially diffusion processes in
the production of semiconductor substrates.
Automatic synthesis of digital systems.
Adaptive devices for various telecommunications tasks.
Image compression.
Radar classification of sea-ice.
Mr.Sanjay Shitole Self-Organized Map
10. Outline
Self-Organized Map
Case Study
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Applications
Architectures
Algorithm
Applications
Statistical pattern recognition, especially recognition of
speech.
Control of robot arms, and other problems in robotics.
Control of industrial process, especially diffusion processes in
the production of semiconductor substrates.
Automatic synthesis of digital systems.
Adaptive devices for various telecommunications tasks.
Image compression.
Radar classification of sea-ice.
Optimization problems.
Mr.Sanjay Shitole Self-Organized Map
11. Outline
Self-Organized Map
Case Study
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Applications
Architectures
Algorithm
Applications
Statistical pattern recognition, especially recognition of
speech.
Control of robot arms, and other problems in robotics.
Control of industrial process, especially diffusion processes in
the production of semiconductor substrates.
Automatic synthesis of digital systems.
Adaptive devices for various telecommunications tasks.
Image compression.
Radar classification of sea-ice.
Optimization problems.
Sentence understanding.
Mr.Sanjay Shitole Self-Organized Map
12. Outline
Self-Organized Map
Case Study
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Applications
Architectures
Algorithm
Applications
Statistical pattern recognition, especially recognition of
speech.
Control of robot arms, and other problems in robotics.
Control of industrial process, especially diffusion processes in
the production of semiconductor substrates.
Automatic synthesis of digital systems.
Adaptive devices for various telecommunications tasks.
Image compression.
Radar classification of sea-ice.
Optimization problems.
Sentence understanding.
Application of expertise in conceptual domain.
Mr.Sanjay Shitole Self-Organized Map
13. Outline
Self-Organized Map
Case Study
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Applications
Architectures
Algorithm
Applications
Statistical pattern recognition, especially recognition of
speech.
Control of robot arms, and other problems in robotics.
Control of industrial process, especially diffusion processes in
the production of semiconductor substrates.
Automatic synthesis of digital systems.
Adaptive devices for various telecommunications tasks.
Image compression.
Radar classification of sea-ice.
Optimization problems.
Sentence understanding.
Application of expertise in conceptual domain.
Classification of insect courtship songs.
Mr.Sanjay Shitole Self-Organized Map
14. Outline
Self-Organized Map
Case Study
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Applications
Architectures
Algorithm
Architectures
Figure: SOM Architecture
Mr.Sanjay Shitole Self-Organized Map
18. Outline
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Applications
Architectures
Algorithm
Algorithm
[1] Initialize the weights Wij (1 < i ≤ n, 1 < j < m) small random
values, where m is the total number of nodes in the map. Set the
initial radius of the neighbourhood around node j as Nj (t).
Mr.Sanjay Shitole Self-Organized Map
19. Outline
Self-Organized Map
Case Study
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Applications
Architectures
Algorithm
Algorithm
[1] Initialize the weights Wij (1 < i ≤ n, 1 < j < m) small random
values, where m is the total number of nodes in the map. Set the
initial radius of the neighbourhood around node j as Nj (t).
2 Present inputs X1(t), X2(t), X3(t), . . . , Xn(t). Where Xi (t) is the
ith input to the node j at time t.
Mr.Sanjay Shitole Self-Organized Map
20. Outline
Self-Organized Map
Case Study
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Applications
Architectures
Algorithm
Algorithm
[1] Initialize the weights Wij (1 < i ≤ n, 1 < j < m) small random
values, where m is the total number of nodes in the map. Set the
initial radius of the neighbourhood around node j as Nj (t).
2 Present inputs X1(t), X2(t), X3(t), . . . , Xn(t). Where Xi (t) is the
ith input to the node j at time t.
3 Calculate the distance dj between the inputs and node j by
dj =
n
i=1
(Xi (t) − Wij (t))2
Mr.Sanjay Shitole Self-Organized Map
21. Outline
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Applications
Architectures
Algorithm
[4] Determine j∗ which minimizes dj .
Mr.Sanjay Shitole Self-Organized Map
22. Outline
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Applications
Architectures
Algorithm
[4] Determine j∗ which minimizes dj .
5 Update weights for j∗ and its neighbours mNj (t), the new weights
for j in Nj∗(t) are Wij (t + 1) = Wij + α(t)(Xi (t) − Wij (t)).
Where α(t) is the learning rate. α(t) and Nj∗(t) are controlled so
as to decrease in t .
Mr.Sanjay Shitole Self-Organized Map
23. Outline
Self-Organized Map
Case Study
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Applications
Architectures
Algorithm
[4] Determine j∗ which minimizes dj .
5 Update weights for j∗ and its neighbours mNj (t), the new weights
for j in Nj∗(t) are Wij (t + 1) = Wij + α(t)(Xi (t) − Wij (t)).
Where α(t) is the learning rate. α(t) and Nj∗(t) are controlled so
as to decrease in t .
6 If process reaches the maximum number of iterations, stop
otherwise go to step 2.
Mr.Sanjay Shitole Self-Organized Map
24. Outline
Self-Organized Map
Case Study
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Land-use Classification
Classification of Antarctic Satellite Imagery
Introduction
Rapid growth of City
Planning
To monitor land use, land cover change
Mr.Sanjay Shitole Self-Organized Map
25. Outline
Self-Organized Map
Case Study
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Land-use Classification
Classification of Antarctic Satellite Imagery
New sensor ASTER data and preparation
Mr.Sanjay Shitole Self-Organized Map
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Land-use Classification
Classification of Antarctic Satellite Imagery
Conclusions and discussion
Table: Training and testing samples
Classes Land cover types Description Training data Testing data
1 Water River, pond 4132 847
2 Forest Planted, nature trees 2797 670
3 Grass Grass, winter wheat 2643 369
4 Farmland Agriculture 5670 1041
5 Roads Highway, city 2712 332
6 Urban Residents, commercial area 7761 1302
7 Other Construction area 1347 171
Total 27062 4722
Mr.Sanjay Shitole Self-Organized Map
27. Outline
Self-Organized Map
Case Study
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Land-use Classification
Classification of Antarctic Satellite Imagery
Conclusions and discussion
Table: The matrix of KSOM classification result
Class Water Forest Grass Farmland Road Urban Other Accuracy (%)
Water 796 0 0 0 37 1 0 95.44
Forest 0 663 0 6 0 0 0 99.10
Grass 0 7 369 0 0 0 0 98.14
Farmland 0 0 0 1018 0 3 0 99.71
Road 49 0 0 0 276 50 0 73.60
Urban 2 0 0 17 9 1248 23 96.07
Other 0 0 0 0 0 0 148 100
Total 847 670 369 1041 322 1302 171
Ground Ref. (%) 93.98 98.96 100 97.79 85.71 95.85 86.55
Mr.Sanjay Shitole Self-Organized Map
28. Outline
Self-Organized Map
Case Study
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Land-use Classification
Classification of Antarctic Satellite Imagery
New sensor ASTER data and preparation
Mr.Sanjay Shitole Self-Organized Map
29. Outline
Self-Organized Map
Case Study
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Land-use Classification
Classification of Antarctic Satellite Imagery
Conclusions
The quality of ASTER data is good for land use mapping.
The KSOM accomplished 95.68% accuracy
7% more accurate than traditional classifier.
Better results are achived for city and farmland classes.
Mr.Sanjay Shitole Self-Organized Map
30. Outline
Self-Organized Map
Case Study
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Land-use Classification
Classification of Antarctic Satellite Imagery
Introduction
1 To study relationship between the Antarctic ice cap and
climatic global warming.
2 Obtain daily analyses of sea ice regions near the Antarctic
bases in order to assist shipping operations.
3 Manual interpretation of remotly sensed data:time-consuming
Mr.Sanjay Shitole Self-Organized Map
31. Outline
Self-Organized Map
Case Study
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Land-use Classification
Classification of Antarctic Satellite Imagery
Figure: Band 1 of the naoo 0.0055 imageMr.Sanjay Shitole Self-Organized Map
32. Outline
Self-Organized Map
Case Study
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Land-use Classification
Classification of Antarctic Satellite Imagery
Figure: Seaice map from noaa.OM image using trained SOM.
Mr.Sanjay Shitole Self-Organized Map
33. Outline
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Jacek M.Zurada, 2001. “Introduction to Artificial Neural
Network”’, Fourth edition, Jaico publishing house.
Simon Haykin, “ Neural Networks, A Comprehensive
Foundation”’, Second Edition, Pearson Education.
Kohonen, T. 1990.“ The Self Organizing Map”’, Proc. IEEE
78(9):1464-1480
Ma Jianwen 1, Hasi Bagan. “Land-use classication using
ASTER data and self-organized neutral networks”,
International Journal of Applied Earth Observation and
Geoinformation 7 (2005) 183188.
D. Kilpatrick and R. Willams, Unsupervised Classification of
Antarctic Satellite Imagery using Kohonen’s Self Organising
Feature Map.,0-7803-2768-3/95/1995 IEEE.
Mr.Sanjay Shitole Self-Organized Map
