Abu-Mostafa Y S
California Institute of Technology, Pasadena 91125, USA.
Neural Comput. 1995 Jul;7(4):639-71. doi: 10.1162/neco.1995.7.4.639.
The systematic use of hints in the learning-from-examples paradigm is the subject of this review. Hints are the properties of the target function that are known to use independently of the training examples. The use of hints is tantamount to combining rules and data in learning, and is compatible with different learning models, optimization techniques, and regularization techniques. The hints are represented to the learning process by virtual examples, and the training examples of the target function are treated on equal footing with the rest of the hints. A balance is achieved between the information provided by the different hints through the choice of objective functions and learning schedules. The Adaptive Minimization algorithm achieves this balance by relating the performance on each hint to the overall performance. The application of hints in forecasting the very noisy foreign-exchange markets is illustrated. On the theoretical side, the information value of hints is contrasted to the complexity value and related to the VC dimension.
在示例学习范式中系统地使用提示是本综述的主题。提示是目标函数的属性，这些属性可独立于训练示例而被知晓并加以利用。使用提示等同于在学习中结合规则和数据，并且与不同的学习模型、优化技术及正则化技术兼容。提示通过虚拟示例呈现给学习过程，目标函数的训练示例与其他提示被同等对待。通过选择目标函数和学习进度，在不同提示所提供的信息之间实现了平衡。自适应最小化算法通过将每个提示上的性能与整体性能相关联来实现这种平衡。文中说明了提示在预测噪声很大的外汇市场中的应用。在理论方面，将提示的信息价值与复杂度值进行了对比，并与VC维相关联。
