Daniel L.Silver
Machine Learning and Applications
•
Sub
-
area of Artificial Intelligence
•
Induction = development of predictive models
from examples
•
Neural networks, decision trees, genetic algorithms
•
Application to Data Mining
–
Medical diagnosis
–
Target marking, web mining
Daniel L.Silver
Machine Learning and Inductive Transfer
Environment
X
Training
Examples
Testing
Examples
(
x, f
(
x
))
Model of
Classifier
h
Inductive
Learning System
short
-
term memory
h
(
x
) ~
f
(
x
)
Domain
Knowledge
long
-
term memory
Retention &
Consolidation
Inductive
Bias
Selection
Knowledge
Transfer
Daniel L.Silver
Coronary Artery Disease Diagnosis
Results on California data (20 training examples) after
learning Cleveland & Hungary models
0.56
0.65
0.67
0.55
0.59
0.73
0.56
0.66
0.66
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Accuracy
Sensitivity
Specificity
Baseline
Task Rehearsal
Task Rehearsal
+ Relatedness
Stream Flow Rate Prediction
Stream flow rate prediction [Lisa Gaudette, 2006]
x
= weather data
f(x)
= flow rate
Daniel L.Silver
Image Transformation
Work withJude Abbey/Liangliang Tu, 2006
-
08
Daniel L.Silver
User Modeling
Intelligent Web Filters
Form Field Ordering
and Completion
Handheld Fashion
Consultant
Smart Email Client
Daniel L.Silver
User Identification
Key Stroke Biometrics
Smart Navigator
Handwriting ID
Eye
-
tracking Biometrics
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