Name
Title | ||
|---|---|---|
Sequence Prediction With Sparse Distributed Hyperdimensional Coding Applied to the Analysis of Mobile Phone Use Patterns. |
Abstract | ||
|---|---|---|
Modeling and prediction of temporal sequences is central to many signal processing and machine learning applications. Prediction based on sequence history is typically performed using parametric models, such as fixed-order Markov chains (n-grams), approximations of high-order Markov processes, such as mixed-order Markov models or mixtures of lagged bigram models, or with other machine learning tec... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TNNLS.2015.2462721 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | DocType | Volume |
Context,Markov processes,Encoding,Predictive models,Data models,History,Standards | Journal | 27 |
Issue | ISSN | Citations |
9 | 2162-237X | 9 |
PageRank | References | Authors |
0.73 | 24 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Okko Räsänen | 1 | 13 | 3.24 |
| Jukka Saarinen | 2 | 264 | 46.21 |