Name
Abstract | ||
|---|---|---|
Markov Model (MM) is a very effective statistical tool for predicting future behavior of a system. The higher the accuracy the more beneficial it becomes. A little increase in accuracy can lead to greater success rate. Improving this accuracy is a big challenge. In this paper we have proposed recursive implementation of MM to achieve better accuracy. We have designed and implemented the algorithm and found average 5% better accuracy than the existing algorithm. This increased accuracy can be helpful in the existing system where MM is used as prediction tool. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/WAINA.2011.63 | AINA Workshops |
Keywords | Field | DocType |
recursive implementation,existing system,existing algorithm,effective statistical tool,increased accuracy,future behavior,better accuracy,big challenge,new approach,greater success rate,markov model,prediction tool,mm,markov processes,accuracy,prediction | Markov process,Markov model,Computer science,Recursive Bayesian estimation,Variable-order Markov model,Artificial intelligence,Hidden Markov model,Machine learning,Recursion | Conference |
ISBN | Citations | PageRank |
978-0-7695-4338-3 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Md. Osman Gani | 1 | 14 | 2.28 |
| Sarah Isnain Binte Ashraf | 2 | 0 | 0.34 |
| Nafia Malik | 3 | 0 | 0.34 |
| Bushra Hossain | 4 | 0 | 0.34 |
| M. Afzal Hossain | 5 | 0 | 0.34 |
| Hasan Sarwar | 6 | 94 | 2.01 |