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Theory of Family of Learnings-From a Single Learning to Infinitely Many Learning-

学习族理论-从单一学习到无限多学习-

基本信息

批准号：
14380158
负责人：
OGAWA Hidemitsu
金额：
$ 7.49万
依托单位：
TOKYO INSTITUTE OF TECHNOLOGY
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2002
资助国家：
日本
起止时间：
2002 至 2004
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-14380158/
关键词：
supervised learning generalization capability projection learning partial projection learning family of projection learning SL projection learning active learning subspace information criterion 誤差逆伝搬法個別学習理論学習族の理論追加学習

项目摘要

In most of the existing supervised learning research, properties of individual learning methods such as the error back-propagation learning method or projection learning have been studied. However, the essence of learning problem can not be elucidated by such individual theories. For example, the error back-propagation algorithm just requires memorization, but it can provide a high level of generalization capability. In order to understand such phenomena, it is important to develop a theory of family of learnings for dealing with infinitely many learnings at the same time, rather than just developing a theory of individual learnings. The head investigator of this project introduced the concept of SL projection learning for the cases where the training input points are fixed, and constructed a theory of family of learnings. This theory enabled us to elucidate many unsolved problems such as the reason why the memorization learning can yield high generalization capability. However, the th … More eory was not easy to apply when the training input points are changing, e.g., in the cases of incremental learning or active learning.In order to extend this theory so that it is applicable to the cases where training input points change, we carried out the following research this year. First, we rigorously defined the notion of "same learning" for different training input points. In the previous work of our group, we have actually given three different definitions of the family of projection learning, and chose the SL projection learning because it is the most natural under fixed training input points. We gave a fresh look at this problem from the viewpoint of "same learning" and showed that T projection learning is more effective than SL projection learning when training input points change. We also elucidated the structure of the space which T operators form. Another important issue to be discussed is incremental active learning, where the next optimal input points are determined based on the learned results obtained so far. We also clarified this problem. Less

在现有的监督学习研究中，大多数是针对个体学习方法的性质进行研究的，如误差反向传播学习方法或投影学习方法。然而，学习问题的本质并不能用这些个别的理论来解释。例如，误差反向传播算法只需要记忆，但它可以提供高水平的泛化能力。为了理解这种现象，重要的是要发展一种学习家族理论，以同时处理无限多的学习，而不仅仅是发展一种个人学习理论。本课题的主要研究人员在训练输入点固定的情况下，引入了SL投影学习的概念，并构建了学习家族理论。这一理论使我们能够解释许多悬而未决的问题，比如为什么记忆学习能够产生高概括能力。然而，TH…更多的理论在训练输入点变化的情况下并不容易应用，例如在增量学习或主动学习的情况下，为了将该理论扩展到适用于训练输入点变化的情况，我们在今年开展了以下研究。首先，我们对不同的训练输入点严格定义了“相同学习”的概念。在我们课题组以前的工作中，我们实际上已经给出了投影学习族的三种不同的定义，并选择了SL投影学习，因为它在固定的训练输入点下是最自然的。我们从“相同学习”的角度对这一问题进行了全新的研究，结果表明，当训练输入点发生变化时，T投影学习比SL投影学习更有效。我们还阐明了T算子所形成的空间结构。另一个要讨论的重要问题是增量式主动学习，其中下一个最优输入点是基于迄今获得的学习结果来确定的。我们也澄清了这个问题。较少