data analysis; learning (artificial intelligence); compact representation; global low dimensional structure; incremental multimanifold out-of-sample data prediction; incremental out-of-sample data low dimensional coordinates prediction approach; intrinsic dimensionality; manifold learning algorithms; multimanifold learning algorithm; out-of-sample data problem; predictive modeling; real-world dataset; synthetic dataset; Accuracy; Algorithm design and analysis; Distributed databases; Manifolds; Matrix decomposition; Measurement; Prediction algorithms; dimensionality reduction; incremental; multi-manifold; out-of-sample data; prediction;
机译:当样本量相对于数据生成过程的复杂性较小时,评估和选择模型以进行样本外预测
机译:从平滑数据开发的树高增量模型的偏向预测
机译:使用响应时间和决策过程模型改善样本外预测
机译:增量多歧管样本外数据预测
机译:稀疏建模用于高维多流形数据分析
机译:引导超出样本的预测以进行有效而准确的交叉验证
机译:有限理性和有限数据集:可测试含义,可识别性和样本外预测