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A novel vector representation of stochastic signals based on adapted ergodic HMMs
Hao Tang
,
Mark Hasegawa-Johnson
, Thomas Huang
Electrical and Computer Engineering
Coordinated Science Lab
Speech and Hearing Science
Linguistics
Beckman Institute for Advanced Science and Technology
Siebel School of Computing and Data Science
Social & Behavioral Sciences Institute
Research output
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peer-review
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Keyphrases
Classification via Regression
20%
Distance Metric Learning
20%
Ergodic
100%
Hidden Markov Model
100%
Image Recognition
20%
Linear Discriminant Analysis
20%
Novel Vectors
100%
Optimal Distance
20%
Pattern Recognition
40%
Pattern Recognition System
40%
Recognition Task
40%
Recognition-based
20%
Signal Base
100%
Stochastic Signals
100%
Vector Representation
100%
Social Sciences
Discriminant Analysis
25%
Hidden Markov Model
100%
Pattern Recognition
100%
Stochastics
100%
Computer Science
Distance Metric
50%
Linear Discriminant Analysis
50%
Pattern Recognition
100%
Pattern Recognition Systems
100%
Engineering
Broad Range
20%
Distance Metric Learning
20%
Pattern Recognition
40%
Pattern Recognition Systems
40%
Representation Vector
100%