Name
Abstract | ||
|---|---|---|
In this paper, prediction for linear systems with missing information is investigated. New methods are introduced to improve the Mean Squared Error (MSE) on the test set in comparison to state-of-the-art methods, through appropriate tuning of Bias-Variance trade-off. First, the use of proposed Soft Weighted Prediction (SWP) algorithm and its efficacy are depicted and compared to previous works for non-missing scenarios. The algorithm is then modified and optimized for missing scenarios. It is shown that controlled over-fitting by suggested algorithms will improve prediction accuracy in various cases. Simulation results approve our heuristics in enhancing the prediction accuracy. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Machine Learning | Data mining,Linear system,Computer science,Linear model,Mean squared error,Heuristics,Missing data,Test set |
DocType | Volume | Citations |
Journal | abs/1701.00677 | 0 |
PageRank | References | Authors |
0.34 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammad Amin Fakharian | 1 | 0 | 0.68 |
| Ashkan Esmaeili | 2 | 2 | 1.85 |
| Farokh Marvasti | 3 | 573 | 72.71 |