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| Paper: | MLSP-P7.7 |
| Session: | Pattern Recognition and Classification II |
| Time: | Friday, May 21, 15:30 - 17:30 |
| Presentation: |
Poster |
| Topic: |
Machine Learning for Signal Processing: Signal detection, Pattern Recognition and Classification |
| Title: |
MULTI-RATE HIDDEN MARKOV MODELS AND THEIR APPLICATION TO MACHINING TOOL-WEAR CLASSIFICATION |
| Authors: |
Özgür Çetin; University of Washington | | |
| | Mari Ostendorf; University of Washington | | |
| Abstract: |
This paper introduces a multi-rate hidden Markov model (multi-rate HMM) for multi-scale stochastic modeling of non-stationary processes. The multi-rate HMM decomposes the process variability into scale-based components, and characterizes both the intra-scale temporal evolution of the process and the inter-scale interactions. Applying these models to the machine tool-wear classification problem in a titanium milling task shows that multi-rate HMMs outperform HMMs in terms of both accuracy and confidence of predictions. |
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