| ICML |
國際機(jī)器學(xué)習(xí)會(huì)議 |
| Identity matrix |
單位矩陣 |
| Image restoration |
圖像復(fù)原 |
| Improved iterative scaling/IIS |
改進(jìn)的迭代尺度法 |
| Incremental learning |
增量學(xué)習(xí) |
| Independent and identically distributed/i.i.d. |
獨(dú)立同分布 |
| Independent Component Analysis/ICA |
獨(dú)立成分分析 |
| Independent subspace analysis |
獨(dú)立子空間分析 |
| Indicator function |
指示函數(shù) |
| Individual learner |
個(gè)體學(xué)習(xí)器 |
| Induction |
歸納 |
| Inductive bias |
歸納偏好 |
| Inductive learning |
歸納學(xué)習(xí) |
| Inductive Logic Programming/ILP |
歸納邏輯程序設(shè)計(jì) |
| Inequality constraint |
不等式約束 |
| Information entropy |
信息熵 |
| Information gain |
信息增益 |
| Input layer |
輸入層 |
| Insensitive loss |
不敏感損失 |
| Inter-cluster similarity |
簇間相似度 |
| International Conference for Machine Learning/ICML |
國際機(jī)器學(xué)習(xí)大會(huì) |
| Intra-cluster similarity |
簇內(nèi)相似度 |
| Intrinsic value |
固有值 |
| Invariance |
不變性 |
| Invert |
求逆 |
| Isometric Mapping/Isomap |
等度量映射 |
| Isotonic regression |
等分回歸 |
| Iterative Dichotomiser |
迭代二分器 |
