| What
is AI?
Specifications
Classifiers Five
Predefined Classifiers: Linear
Discriminant Function
 |
K-Nearest
Neighbor
|
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Empirical
Bayesian
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Minimum
Distance
|
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Neural
Network
|
Number of Pattern
Classes Supported: unlimited
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Hierarchical
multi-level classifier definition
setup
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Training/testing
set swapping
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Export
trained classifier
|
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Anomaly
detection
|
Features Number of
Predefined Waveform Feature Sets: 6
Waveform Signal
Feature Domains: 6
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Time
Domain
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Power
Domain
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Phase
Domain
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Cepstral
Domain
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Auto-correlation
Domain
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Cross-correlation
Domain
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User-defined feature
sets: for image and
other applications
Size of feature
vector: unlimited
Feature Analysis
Aids:
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Automatic
Feature Ranking
|
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Fisher
and Bhattacharyya distances
|
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Feature
Probability Density Function Analysis
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3D
Feature/Feature Probability Plots
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Density
Function Projection
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Feature
File/Record Browsing
|
Operating Modes:
|
Visual
classifier setup including
hierarchical setup
|
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Classifier
Training
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Classifier
Evaluation
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Identification
of Unknown Signals
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Integration and
Deployment
The classifier is
ready to use with your final target system
code mingling: ICEPak's
classification and feature extraction
engines are self-contained encapsulated
modules
|
Export
feature extraction engine
|
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Export
classification engine
|
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Rapid
prototype creation
|
|
Ready
to run sample programs with source
code
|
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Implementation
Libraries ANSI C API
|
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Visual
Basic API
|
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C++
Class library
|
Data Formats
Supported Single byte integer
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Short
integer (double byte)
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Long
integer (4 bytes)
|
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Signed
and unsigned integer support
|
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Floating
point (4 bytes)
|
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Double
floating point (8 bytes)
|
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Big-endian
and little-endian support
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Documentation
Extensive on-line HTML reference
|
Getting
started --- to bring you up to speed
in no time
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Tutorial
--- guide you through steps of
designing your first classifier
|
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Theory
of operation --- everything you want
to know about the inner working of the
system
|
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Advanced
topics --- information you need to
write your own application
|
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