Download - … SANN … Classification …
![Page 1: … SANN … Classification …](https://reader036.vdocuments.site/reader036/viewer/2022062519/56815044550346895dbe445a/html5/thumbnails/1.jpg)
… SANN … Classification …
Classification with
Subsequent Artificial Neural Networks
Linder, Dew, Sudhoff, Theegarten, Remberger, PÖppl, Wagner
Brian Selinsky
![Page 2: … SANN … Classification …](https://reader036.vdocuments.site/reader036/viewer/2022062519/56815044550346895dbe445a/html5/thumbnails/2.jpg)
Outline
• Terminology
• SANN vs Other ANN approaches
• SANN vs All Pairs
• Results
![Page 3: … SANN … Classification …](https://reader036.vdocuments.site/reader036/viewer/2022062519/56815044550346895dbe445a/html5/thumbnails/3.jpg)
Terminology
• Clustering– Dimensionality
• NN vs. ANN• Neuron• Synapse• Thresholds• Weights• Training
– Learning Rate– Backpropagation
• Input– Standardization– Normalization
• Hidden Layers• Approaches
– MLP– One vs All– All Pairs– SANN
• Tools
![Page 4: … SANN … Classification …](https://reader036.vdocuments.site/reader036/viewer/2022062519/56815044550346895dbe445a/html5/thumbnails/4.jpg)
Clustering
OO
O
OOO
OOOO
XX
X
XXX
XXXX
![Page 5: … SANN … Classification …](https://reader036.vdocuments.site/reader036/viewer/2022062519/56815044550346895dbe445a/html5/thumbnails/5.jpg)
Clustering
OO
O
OOO
OOOO
XX
X
XXX
XXXX
YY
Y
YYY
YYYY
![Page 6: … SANN … Classification …](https://reader036.vdocuments.site/reader036/viewer/2022062519/56815044550346895dbe445a/html5/thumbnails/6.jpg)
Clustering
OO
O
OOO
OOOO
XX
X
XXX
XXXX
YY
Y
YYY
YYYY
![Page 7: … SANN … Classification …](https://reader036.vdocuments.site/reader036/viewer/2022062519/56815044550346895dbe445a/html5/thumbnails/7.jpg)
Dimensionality
• Inputs of interest
• Hyperplanes
• Dr Frisinas’ data– 4 Clusters– 22690 Inputs of interest– 22690 Dimensional Data– 3 or 4 - 22689 Dimensional Hyperplanes
![Page 8: … SANN … Classification …](https://reader036.vdocuments.site/reader036/viewer/2022062519/56815044550346895dbe445a/html5/thumbnails/8.jpg)
Dimensionality
• Neural Nets convert input dimensionality to 1 number!
-1 0 1
![Page 9: … SANN … Classification …](https://reader036.vdocuments.site/reader036/viewer/2022062519/56815044550346895dbe445a/html5/thumbnails/9.jpg)
ANN vs. NN
• Semantics
• Artificial Neural Net– Meant to simulate how the brain functions– Brain is a network of neurons– Brain is the natural neural net
• I use NN
![Page 10: … SANN … Classification …](https://reader036.vdocuments.site/reader036/viewer/2022062519/56815044550346895dbe445a/html5/thumbnails/10.jpg)
Neural Net
Neural Net Black Box(Some magic happens here)
Class 1
Class 2
Class 3
![Page 11: … SANN … Classification …](https://reader036.vdocuments.site/reader036/viewer/2022062519/56815044550346895dbe445a/html5/thumbnails/11.jpg)
Neuron
CalculateSummation
Compare toThreshold
Class 1
Class 2
Class 3
Neural Net
![Page 12: … SANN … Classification …](https://reader036.vdocuments.site/reader036/viewer/2022062519/56815044550346895dbe445a/html5/thumbnails/12.jpg)
Neural Network
N
C T
C1
C2
C3
N
N
N
N
N
N Neural Net
![Page 13: … SANN … Classification …](https://reader036.vdocuments.site/reader036/viewer/2022062519/56815044550346895dbe445a/html5/thumbnails/13.jpg)
Neural Network
N
C T
C1
C2
C3
N
N
N
N
N
N Neural Net
Inputs & Processing
Learning
![Page 14: … SANN … Classification …](https://reader036.vdocuments.site/reader036/viewer/2022062519/56815044550346895dbe445a/html5/thumbnails/14.jpg)
Training
N
C T
C1
C2
C3
N
N
N
N
N
N Neural Net
Inputs & Processing
Learning
Training Set
![Page 15: … SANN … Classification …](https://reader036.vdocuments.site/reader036/viewer/2022062519/56815044550346895dbe445a/html5/thumbnails/15.jpg)
What gets trained
• Threshold– Categorization
• Weight– Impact of an input to a neuron– Proportionality
• Learning Rate– Effect on weights– Effect on speed of training
![Page 16: … SANN … Classification …](https://reader036.vdocuments.site/reader036/viewer/2022062519/56815044550346895dbe445a/html5/thumbnails/16.jpg)
How? - Backpropagation
N
C T
C1
C2
C3
N
N
N
N
N
N Neural Net
Inputs & Processing
Learning
Training Set
![Page 17: … SANN … Classification …](https://reader036.vdocuments.site/reader036/viewer/2022062519/56815044550346895dbe445a/html5/thumbnails/17.jpg)
Input Data
• Data 1 Range 12000 – 500000
• Data 2 Range 1.0 – 1.5
• Standardizing or normalizing data makes weights more consistent and more accurate
![Page 18: … SANN … Classification …](https://reader036.vdocuments.site/reader036/viewer/2022062519/56815044550346895dbe445a/html5/thumbnails/18.jpg)
Approaches
• Multi-layer Perceptron (MLP)
• Subdividing the problem– One vs. All– All Pairs– SANN
![Page 19: … SANN … Classification …](https://reader036.vdocuments.site/reader036/viewer/2022062519/56815044550346895dbe445a/html5/thumbnails/19.jpg)
One vs. All
ANN
ANN
ANN
Class A
Class C
Class B
Not Class C
Not Class B
Not Class A
![Page 20: … SANN … Classification …](https://reader036.vdocuments.site/reader036/viewer/2022062519/56815044550346895dbe445a/html5/thumbnails/20.jpg)
All Pairs
ANN
ANN
ANN
Class A
Class B
Class A
Class C
Class C
Class B
Class A
Class B
Class C
![Page 21: … SANN … Classification …](https://reader036.vdocuments.site/reader036/viewer/2022062519/56815044550346895dbe445a/html5/thumbnails/21.jpg)
SANN
ANN
ANN A vs B
ANN A vs C
Class A .12
Class C .91Class B .88
ANN B vs CClass B .90
Class C .89Final Values
Class A .12
Class B .90
Class C .89
![Page 22: … SANN … Classification …](https://reader036.vdocuments.site/reader036/viewer/2022062519/56815044550346895dbe445a/html5/thumbnails/22.jpg)
Results
• Increased data & nodes– Increased noise
• Subdividing NNs increases accuracy
• All Pairs vs SANN– All Pairs more accurate– SANN faster
![Page 23: … SANN … Classification …](https://reader036.vdocuments.site/reader036/viewer/2022062519/56815044550346895dbe445a/html5/thumbnails/23.jpg)
Tools (FYI)
• MatLab (Neural Network Toolbox)– On CS System (Unix and Windows)
• NeuroSolutions– 60 day free trial (Windows)
• Joone– Free (Platform Independent)