Name
Abstract | ||
|---|---|---|
Additive models are regression methods which model the response variable as the sum of univariate transfer functions of the input variables. Key benefits of additive models are their accuracy and interpretability on many real-world tasks. Additive models are however not adapted to problems involving a large number (e.g., hundreds) of input variables, as they are prone to overfitting in addition to... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TKDE.2016.2525996 | IEEE Transactions on Knowledge and Data Engineering |
Keywords | Field | DocType |
Input variables,Additives,Data models,Transfer functions,Adaptation models,Load modeling,Indexes | Interpretability,Dimensionality reduction,Additive model,Identifiability,Computer science,Nonparametric regression,Projection pursuit regression,Artificial intelligence,Overfitting,Univariate,Machine learning | Journal |
Volume | Issue | ISSN |
28 | 6 | 1041-4347 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alhussein Fawzi | 1 | 766 | 36.80 |
| Jean-Baptiste Fiot | 2 | 10 | 1.98 |
| Bei Chen | 3 | 26 | 9.11 |
| Mathieu Sinn | 4 | 10 | 3.65 |
| Pascal Frossard | 5 | 3015 | 230.41 |