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| No. | 单词 | 释义 |
|---|---|---|
| 201 | Forward Cross-Validation | 前向交叉验证 |
| 202 | Forward Prediction | 前向预测 |
| 203 | Forward Reaction Prediction | 前向反应预测 |
| 204 | Fuzzy Logic | 模糊逻辑 |
| 205 | Fuzzy Neural Networks | 模糊神经网络 |
| 206 | Ga-Based Approaches | 基于遗传算法的方法 |
| 207 | Garbage In | Garbage Out |
| 208 | Gas-Phase Networks | 气相网络 |
| 209 | Gaussian Distribution | 高斯分布 |
| 210 | Gaussian Kernel Function | 高斯核函数 |
| 211 | Gaussian Kernels | 高斯核 |
| 212 | Gaussian Mixtures | 高斯混合（模型） |
| 213 | Gaussian Process | 高斯过程 |
| 214 | Gaussian Process Regression | 高斯过程回归 |
| 215 | Gaussian-Type Structure Descriptors | 高斯型结构描述符 |
| 216 | General Intelligence | 通用智能 |
| 217 | Generalized Gradient Approximation | 广义梯度近似 |
| 218 | Generative Adversarial Networks | 生成对抗网络 |
| 219 | Generative Modeling | 生成式建模 |
| 220 | Genetic Algorithm | 遗传算法 |
| 221 | Gradient Boosting Decision Tree | 梯度提升决策树 |
| 222 | Gradient Descent | 梯度下降 |
| 223 | Gradient-Based | 基于梯度的 |
| 224 | Grain-Surface Networks | 粒面网络 |
| 225 | Graph Convolutional | 图卷积 |
| 226 | Graph Models | 图模型 |
| 227 | Graph Neural Networks | 图神经网络 |
| 228 | Graph-Based | 基于图形 |
| 229 | Graph-Based Models | 基于图的模型 |
| 230 | Graph-Based Neural Networks | 基于图的神经网络 |
| 231 | Graph-Based Representation | 基于图的表示 |
| 232 | Graph-Convolutional Neural Network | 图卷积神经网络 |
| 233 | Graphics Processing Units | 图形处理器 |
| 234 | Gravimetric Polymerization Degree | 比重聚合度 |
| 235 | Grid Search | 网格搜索 |
| 236 | Ground Truth | 真实值 |
| 237 | Hamiltonian Matrix | 哈密顿矩阵 |
| 238 | Hamiltonian Operator | 哈密顿算符 |
| 239 | Heterogeneous Data | 异构数据 |
| 240 | Hidden Layers | 隐藏层 |
| 241 | High Data Throughput | 高数据吞吐量 |
| 242 | High Throughput | 高通量 |
| 243 | High Throughput Screening | 高通量筛选 |
| 244 | High Variance Models | 高方差模型 |
| 245 | High-Dimensional Data | 高维数据 |
| 246 | High-Dimensional NN | 高维神经网络 |
| 247 | High-Dimensional Objects | 高维对象 |
| 248 | High-Throughput | 高通量 |
| 249 | Higher-Dimensional Space | 高维空间 |
| 250 | Higher-Dimensional Spectral Space | 高维光谱空间 |
| 251 | Homogenization | 同质化 |
| 252 | Homomorphic Encryption | 同态加密 |
| 253 | Human Face Recognition | 人脸识别 |
| 254 | Human-Encoded | 人工编码的 |
| 255 | Hybrid Model | 混合模型 |
| 256 | Hybrid Technique | 混合技术 |
| 257 | Hybrid-Neural Model | 混合神经模型 |
| 258 | Hyperparameter Opimization | 超参数优化 |
| 259 | Hyperparameters | 超参数 |
| 260 | Hyperplane | 超平面 |
| 261 | Hyperplanes Separate | 超平面分离 |
| 262 | Id3 Algorithm | Id3 算法 |
| 263 | Image And Speech Recognition | 图像和语音识别 |
| 264 | Image Classification | 图像分类 |
| 265 | Image Classifier | 图像分类器 |
| 266 | Image Recognition | 图像识别 |
| 267 | Inductive Bias | 归纳偏好 |
| 268 | Information Gain | 信息增益 |
| 269 | Information Gain Ratio | 信息增益比 |
| 270 | Informative Priors | 信息先验 |
| 271 | Input-Output Pairs | 输入输出对 |
| 272 | Instance-Based | 基于实例的 |
| 273 | Intelligent Machine | 智能机器 |
| 274 | Intermediate Neurons | 中间神经元 |
| 275 | Internet Of Things | 物联网 |
| 276 | Interpolation Coordinate | 插值坐标 |
| 277 | Interpretability | 可解释性 |
| 278 | Inverse Neural Modeling | 逆神经建模 |
| 279 | Inverse Neural Network Modeling | 逆神经网络建模 |
| 280 | Iteration | 迭代 |
| 281 | Iterative Learning | 迭代学习 |
| 282 | Joint Distribution | 联合分布 |
| 283 | Jordan-Elman Neural Networks | Jordan-Elman 神经网络 |
| 284 | K Clusters | K聚类 |
| 285 | K Nearest Points | K 最近点 |
| 286 | K-1 Folds | K-1 折 |
| 287 | K-Edge (O-K Edge) | K-边缘（O-K 边缘） |
| 288 | K-Fold Cross Validation | k 折交叉验证 |
| 289 | K-Means | K-均值 |
| 290 | K-Means Clustering | k-均值聚类 |
| 291 | K-Nearest Neighbor Method | k-近邻 |
| 292 | KNN Model | K 近邻模型 |
| 293 | Kendall’S Tau | 肯德尔等级相关系数 |
| 294 | Kernel Method | 核方法 |
| 295 | Kernel Ridge Regression | 核岭回归 |
| 296 | Kernel Trick | 核技巧 |
| 297 | Kernels | 内核 |
| 298 | Kinetic Curve | 动力学曲线 |
| 299 | Knowledge Extraction | 知识提取 |
| 300 | Knowledge Gradient | 知识梯度 |
| 301 | L1 And L2 Regularization | L1与L2正则化 |
| 302 | LBP | 局部二值模式 |
| 303 | Label | 标签/标记 |
| 304 | Laboratory Level | 实验室级别 |
| 305 | Language Processing | 语言处理 |
| 306 | Laplacian Prior | 拉普拉斯先验 |
| 307 | Large-Scale Data Storage | 大规模数据存储 |
| 308 | Lasers | 激光器 |
| 309 | Lasso Regression | 拉索回归 |
| 310 | Lazy Learning | 懒惰学习 |
| 311 | Least Absolute Shrinkage And Selection Operator | Lasso回归 |
| 312 | Least Square Support Vector Machine | 最小二乘支持向量机 |
| 313 | Ligand-Field | 配位场 |
| 314 | Linear | 线性的 |
| 315 | Linear Combination | 线性组合 |
| 316 | Linear Dimension Reduction Methods | 线性降维方法 |
| 317 | Linear Discriminant Analysis | 线性判别分析 |
| 318 | Linear Model | 线性模型 |
| 319 | Linear Regression | 线性回归 |
| 320 | Linear Vibronic Coupling Model | 线性振子耦合模型 |
| 321 | Local Recurrent | 本地卷积 |
| 322 | Logic And Heuristics Applied To Synthetic Analysis | LHASA 程序 |
| 323 | Logistic Function | 对数几率函数 |
| 324 | Logistic Regression | 对数几率回归 |
| 325 | Long Short Term Memory | 长短期记忆 |
| 326 | Long-Range Prediction | 长期预测 |
| 327 | Long-Range Prediction Models | 长期预测模型 |
| 328 | Long-Term Planning | 长期规划 |
| 329 | Long-Term Reward | 长期回报 |
| 330 | Loss Function | 损失函数 |
| 331 | MCTS Method | 蒙特卡洛树搜索方法 |
| 332 | ML Algorithm | 机器学习算法 |
| 333 | ML Modelling | 机器学习建模 |
| 334 | ML Potentials | 机器学习势能 |
| 335 | ML-Driven | 机器学习驱动的 |
| 336 | ML-Driven Optimization | 机器学习驱动的最优化 |
| 337 | MLP Neural Model | 多层感知机神经模型 |
| 338 | Machine Learning | 机器学习 |
| 339 | Machine-Readable Data | 机器可读的数据 |
| 340 | Mae | 平均绝对误差 |
| 341 | Mahalanobis Distances | 马氏距离 |
| 342 | Margin | 间隔 |
| 343 | Matrices | 矩阵 |
| 344 | Matthews Correlation Coefficient | 马修斯相关系数 |
| 345 | Maximum Likelihood Methods | 最大似然法 |
| 346 | Maximum Likelihood Procedures | 最大似然估计法 |
| 347 | Mean-Squared Error | 均方误差 |
| 348 | Mechanical Sympathy | 机械同感，软硬件协同编程 |
| 349 | Merging | 合并 |
| 350 | Message Passing Neural Networks | 消息传递神经网络 |
| 351 | Meta-Learning | 元学习 |
| 352 | Metric | 指标 |
| 353 | Microarray Data | 微阵列数据 |
| 354 | Mini Batch | 小批次 |
| 355 | Mining | 挖掘 |
| 356 | Mining Out | 挖掘 |
| 357 | Missing Values | 缺失值 |
| 358 | Model Construction | 模型构建 |
| 359 | Model Evaluation | 模型评估 |
| 360 | Model Performance | 模型性能 |
| 361 | Model Predictive Control | 模型预测控制 |
| 362 | Model Selection | 模型选择 |
| 363 | Model Statistics | 模型统计 |
| 364 | Model Training | 模型训练 |
| 365 | Model Validation | 模型验证 |
| 366 | Model-Based Iterative Reconstruction | 基于模型的迭代重建 |
| 367 | Model-Construction | 模型构建 |
| 368 | Modelling Scenario | 建模场景 |
| 369 | Molecular Graph Theory | 分子图论 |
| 370 | Molecular Modelling | 分子建模 |
| 371 | Monte Carlo Tree Search | 蒙特卡洛树搜索 |
| 372 | Moore’S Law | 摩尔定律 |
| 373 | Multi-Agent Control System | 多智能体控制系统 |
| 374 | Multi-Core Desktop Computer | 多核台式计算机 |
| 375 | Multi-Dimensional Big Data Analysis | 多维度大数据分析 |
| 376 | Multi-Layer Feed-Forward | 多层前馈 |
| 377 | Multi-Layer Perceptron | 多层感知机 |
| 378 | Multi-Objective Genetic Algorithm | 多目标遗传算法 |
| 379 | Multi-Objective Optimization | 多目标优化 |
| 380 | Multi-Reaction Synthesis | 多反应合成 |
| 381 | Multilayer Perceptron | 多层感知机 |
| 382 | Multiple Linear Regression | 多元线性回归 |
| 383 | Multivariate Regression | 多变量回归 |
| 384 | N-Dimensional Space | N维空间 |
| 385 | Naive Bayesian | 朴素贝叶斯 |
| 386 | Naive Bayesian Methods | 朴素贝叶斯方法 |
| 387 | Named Entity Recognition，NER | 命名实体识别 |
| 388 | Natural Language Processing | 自然语言处理 |
| 389 | Nearest Neighbors | 近邻 |
| 390 | Nearest Neighbour Model | 近邻模型 |
| 391 | Negative Predictive Value | 阴性预测值 |
| 392 | Network Architecture | 网络结构 |
| 393 | Network Geometry | 网络几何 |
| 394 | Neural Model | 神经模型 |
| 395 | Neural Network | 神经网络 |
| 396 | Neural Turing Machines | 神经图灵机 |
| 397 | Neural-Network-Based Function | 基于神经网络的函数 |
| 398 | Neurons | 神经元 |
| 399 | Noise | 噪声 |
| 400 | Noise Filters | 噪声过滤器 |