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首页> 外文期刊>Neural Networks: The Official Journal of the International Neural Network Society >Real-time model learning using Incremental Sparse Spectrum Gaussian Process Regression
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Real-time model learning using Incremental Sparse Spectrum Gaussian Process Regression

机译：使用增量稀疏谱高斯过程回归的实时模型学习

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摘要

Novel applications in unstructured and non-stationary human environments require robots that learn from experience and adapt autonomously to changing conditions. Predictive models therefore not only need to be accurate, but should also be updated incrementally in real-time and require minimal human intervention. Incremental Sparse Spectrum Gaussian Process Regression is an algorithm that is targeted specifically for use in this context. Rather than developing a novel algorithm from the ground up, the method is based on the thoroughly studied Gaussian Process Regression algorithm, therefore ensuring a solid theoretical foundation. Non-linearity and a bounded update complexity are achieved simultaneously by means of a finite dimensional random feature mapping that approximates a kernel function. As a result, the computational cost for each update remains constant over time. Finally, algorithmic simplicity and support for automated hyperparameter optimization ensures convenience when employed in practice. Empirical validation on a number of synthetic and real-life learning problems confirms that the performance of Incremental Sparse Spectrum Gaussian Process Regression is superior with respect to the popular Locally Weighted Projection Regression, while computational requirements are found to be significantly lower. The method is therefore particularly suited for learning with real-time constraints or when computational resources are limited.
机译：在非结构化和非平稳的人类环境中的新应用需要机器人从经验中学习并自动适应不断变化的条件。因此，预测模型不仅需要准确，而且还应实时进行增量更新，并且需要最少的人工干预。增量稀疏谱高斯过程回归算法是专门针对这种情况而设计的一种算法。该方法不是从头开始开发一种新颖的算法，而是基于经过深入研究的高斯过程回归算法，从而确保了坚实的理论基础。非线性和有界更新复杂性是通过近似内核函数的有限维随机特征映射同时实现的。结果，每次更新的计算成本随时间保持不变。最后，算法的简单性和对自动超参数优化的支持确保了在实践中使用时的便利性。对许多综合和现实学习问题的经验验证证实，相对于流行的局部加权投影回归，增量稀疏谱高斯过程回归的性能要好，而计算要求却要低得多。因此，该方法特别适合于具有实时约束或计算资源有限的学习。

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