Projects per year
Abstract
We consider the problem of training a Hidden Markov Model
(HMM) from fully observable data and predicting the hidden states of an
observed sequence. Our attention is focused to applications that require
a list of potential sequences as a prediction. We propose a novel method
based on Conformal Prediction (CP) that, for an arbitrary confidence
level 1 − ε, produces a list of candidate sequences that contains the correct
sequence of hidden states with probability at least 1−ε. We present
experimental results that confirm this holds in practice. We compare our
method with the standard approach (i.e.: the use of Maximum Likelihood
and the List–Viterbi algorithm), which suffers from violations to
the assumed distribution. We discuss advantages and limitations of our
method, and suggest future directions.
(HMM) from fully observable data and predicting the hidden states of an
observed sequence. Our attention is focused to applications that require
a list of potential sequences as a prediction. We propose a novel method
based on Conformal Prediction (CP) that, for an arbitrary confidence
level 1 − ε, produces a list of candidate sequences that contains the correct
sequence of hidden states with probability at least 1−ε. We present
experimental results that confirm this holds in practice. We compare our
method with the standard approach (i.e.: the use of Maximum Likelihood
and the List–Viterbi algorithm), which suffers from violations to
the assumed distribution. We discuss advantages and limitations of our
method, and suggest future directions.
Original language | English |
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Title of host publication | Conformal and Probabilistic Prediction with Applications |
Subtitle of host publication | 5th International Symposium, COPA 2016 Madrid, Spain, April 20–22, 2016 Proceedings |
Publisher | Springer |
Pages | 128-144 |
Number of pages | 17 |
Volume | 9653 |
ISBN (Electronic) | 978-3-319-33395-3 |
ISBN (Print) | 978-3-319-33394-6 |
DOIs | |
Publication status | Published - 17 Apr 2016 |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 9653 |
ISSN (Print) | 0302-9743 |
Projects
- 1 Finished
-
Centre for Doctoral Training in Cyber Security
Cid, C. (PI), Crampton, J. (CoI), Martin, K. M. (CoI) & Paterson, K. (CoI)
Eng & Phys Sci Res Council EPSRC
1/04/13 → 31/12/19
Project: Research
Activities
- 1 Participation in conference
-
Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2016)
Giovanni Cherubin (Speaker)
2016Activity: Participating in or organising an event › Participation in conference