Name
Abstract | ||
|---|---|---|
We consider the problem of inferring a sequence of hidden states associated with a sequence of observations produced by an individual within a population. Instead of learning a single sequence model for the population (which does not account for variations within the population), we learn a set of basis sequence models based on different individuals. The sequence of hidden states for a new individual is inferred in an online fashion by estimating a distribution over the basis models that best explain the sequence of observations of this new individual. We explain how to do this in the context of hidden Markov models with Gaussian mixture models that are learned based on streaming data by online Bayesian moment matching. The resulting transfer learning technique is demonstrated with three real-word applications: activity recognition based on smartphone sensors, sleep classification based on electroencephalography data and the prediction of the direction of future packet flows between a pair of servers in telecommunication networks. |
Year | Venue | Field |
|---|---|---|
2017 | international conference on learning representations | Population,Data mining,Computer science,Server,Transfer of learning,Unsupervised learning,Artificial intelligence,Activity recognition,Pattern recognition,Hidden Markov model,Mixture model,Machine learning,Bayesian probability |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
9 | 10 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Priyank Jaini | 1 | 7 | 4.57 |
| Zhitang Chen | 2 | 43 | 13.38 |
| Pablo Carbajal | 3 | 1 | 0.35 |
| Edith Law | 4 | 348 | 43.02 |
| Laura E. Middleton | 5 | 1 | 0.35 |
| Kayla Regan | 6 | 1 | 0.35 |
| Mike Schaekermann | 7 | 5 | 5.26 |
| George Trimponias | 8 | 12 | 5.65 |
| James Yungjen Tung | 9 | 22 | 4.59 |
| Pascal Poupart | 10 | 1352 | 105.24 |