Adaptation in statistical pattern recognition using tangent vectors.
We integrate the tangent method into a statistical framework for classification analytically and practically. The resulting consistent framework for adaptation allows us to efficiently estimate the tangent vectors representing the variability. The framework improves classification results on two real-world pattern recognition tasks from the domains handwritten character recognition and automatic speech recognition.
Keysers
Daniel
D
Lehrstuhl für Informatik VI, Computer Science Department, RWTH Aachen-University of Technology, D-52056 Aachen, Germany. keysers@informatik.rwth-aachen.de
Macherey
Wolfgang
W
Ney
Hermann
H
Dahmen
Jörg
J
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D001330:Electronic Data Processing; D005246:Feedback; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D015233:Models, Statistical; D009323:Natural Language Processing; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated; D011932:Reading; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013067:Speech Perception; D013382:Subtraction Technique