| Radial Basis Function/RBF |
徑向基函數(shù) |
| Random Forest Algorithm |
隨機(jī)森林算法 |
| Random walk |
隨機(jī)漫步 |
| Recall |
查全率／召回率 |
| Receiver Operating Characteristic/ROC |
受試者工作特征 |
| Rectified Linear Unit/ReLU |
線性修正單元 |
| Recurrent Neural Network |
循環(huán)神經(jīng)網(wǎng)絡(luò) |
| Recursive neural network |
遞歸神經(jīng)網(wǎng)絡(luò) |
| Reference model |
參考模型 |
| Regression |
回歸 |
| Regularization |
正則化 |
| Regularizer |
正則化項(xiàng) |
| Reinforcement learning/RL |
強(qiáng)化學(xué)習(xí) |
| Relative entropy |
相對(duì)熵 |
| Reparametrization |
重參數(shù)化 |
| Representation learning |
表征學(xué)習(xí) |
| Representer theorem |
表示定理 |
| Reproducing Kernel Hilbert Space/RKHS |
再生核希爾伯特空間 |
| Re-sampling |
重采樣法 |
| Rescaling |
再縮放 |
| Reservoir computing |
儲(chǔ)層計(jì)算 |
| Residual Mapping |
殘差映射 |
| Residual Network |
殘差網(wǎng)絡(luò) |
| Restricted Boltzmann Machine/RBM |
受限玻爾茲曼機(jī) |
| Restricted Isometry Property/RIP |
限定等距性 |
| Reverse mode accumulation |
反向模式累加 |
| Re-weighting |
重賦權(quán)法 |
| Ridge regression |
嶺回歸 |
| Robustness |
穩(wěn)健性/魯棒性 |
| Root node |
根結(jié)點(diǎn) |
| Rule Engine |
規(guī)則引擎 |
| Rule learning |
規(guī)則學(xué)習(xí) |
