… sann … classification …
DESCRIPTION
… SANN … Classification …. Classification with Subsequent Artificial Neural Networks Linder, Dew, Sudhoff, Theegarten, Remberger, P Ö ppl, Wagner. Brian Selinsky. Outline. Terminology SANN vs Other ANN approaches SANN vs All Pairs Results. Clustering Dimensionality NN vs. ANN Neuron - PowerPoint PPT PresentationTRANSCRIPT
… SANN … Classification …
Classification with
Subsequent Artificial Neural Networks
Linder, Dew, Sudhoff, Theegarten, Remberger, PÖppl, Wagner
Brian Selinsky
Outline
• Terminology
• SANN vs Other ANN approaches
• SANN vs All Pairs
• Results
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
Clustering
OO
O
OOO
OOOO
XX
X
XXX
XXXX
Clustering
OO
O
OOO
OOOO
XX
X
XXX
XXXX
YY
Y
YYY
YYYY
Clustering
OO
O
OOO
OOOO
XX
X
XXX
XXXX
YY
Y
YYY
YYYY
Dimensionality
• Inputs of interest
• Hyperplanes
• Dr Frisinas’ data– 4 Clusters– 22690 Inputs of interest– 22690 Dimensional Data– 3 or 4 - 22689 Dimensional Hyperplanes
Dimensionality
• Neural Nets convert input dimensionality to 1 number!
-1 0 1
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
Neural Net
Neural Net Black Box(Some magic happens here)
Class 1
Class 2
Class 3
Neuron
CalculateSummation
Compare toThreshold
Class 1
Class 2
Class 3
Neural Net
Neural Network
N
C T
C1
C2
C3
N
N
N
N
N
N Neural Net
Neural Network
N
C T
C1
C2
C3
N
N
N
N
N
N Neural Net
Inputs & Processing
Learning
Training
N
C T
C1
C2
C3
N
N
N
N
N
N Neural Net
Inputs & Processing
Learning
Training Set
What gets trained
• Threshold– Categorization
• Weight– Impact of an input to a neuron– Proportionality
• Learning Rate– Effect on weights– Effect on speed of training
How? - Backpropagation
N
C T
C1
C2
C3
N
N
N
N
N
N Neural Net
Inputs & Processing
Learning
Training Set
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
Approaches
• Multi-layer Perceptron (MLP)
• Subdividing the problem– One vs. All– All Pairs– SANN
One vs. All
ANN
ANN
ANN
Class A
Class C
Class B
Not Class C
Not Class B
Not Class A
All Pairs
ANN
ANN
ANN
Class A
Class B
Class A
Class C
Class C
Class B
Class A
Class B
Class C
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
Results
• Increased data & nodes– Increased noise
• Subdividing NNs increases accuracy
• All Pairs vs SANN– All Pairs more accurate– SANN faster
Tools (FYI)
• MatLab (Neural Network Toolbox)– On CS System (Unix and Windows)
• NeuroSolutions– 60 day free trial (Windows)
• Joone– Free (Platform Independent)