论文标题
在线顺序极端学习机：数百个中间层的功能
Online Sequential Extreme Learning Machines: Features Combined From Hundreds of Midlayers
论文作者
论文摘要
在本文中，我们开发了一种称为层次在线顺序学习算法（H-OS-ELM）的算法，用于单个饲料馈电网络，并具有数百个中间层的功能，该算法通过块学习构成的构造可以与固定或变化的块大小相结合，我们相信，通过较大的层次进行了较高的型号，可以在较高的层次上进行分配的范围，而这些层次的分配了型号的范围，而这些层次的分配了副本，这些信息的范围是编码范围的，这些信息的范围是编码范围的，这些算法的编码范围的范围是构造的，这些算法的编码范围是编码的，这些算法的构造范围是编码的范围，并且可以提供编码范围的范围。 准确性。因此，本文提出了一个分层模型框架与在线序列学习算法相结合，首先是由由子网络神经元组成的子空间提取器组成的模型，使用子网神经元组成，使用子网神经元，这是由层次结构的第一层中的特征提取器导致的，我们从层次结构中脱颖而出，这是不相关的模型，该模型是在学习和估计中，因此，该过程均可恢复过来，因此，该过程均可恢复过来，从而使一系列估算的效果，因此，该过程逐步使用。被处理成更可接受的认知。其次，通过使用OS-ELM，我们正在使用非著作风格进行学习
In this paper, we develop an algorithm called hierarchal online sequential learning algorithm (H-OS-ELM) for single feed feedforward network with features combined from hundreds of midlayers, the algorithm can learn chunk by chunk with fixed or varying block size, we believe that the diverse selectivity of neurons in top layers which consists of encoded distributed information produced by the other neurons offers better computational advantage over inference accuracy. Thus this paper proposes a Hierarchical model framework combined with Online-Sequential learning algorithm, Firstly the model consists of subspace feature extractor which consists of subnetwork neuron, using the sub-features which is result of the feature extractor in first layer of the hierarchy we get rid of irrelevant factors which are of no use for the learning and iterate this process so that to recast the the subfeatures into the hierarchical model to be processed into more acceptable cognition. Secondly by using OS-Elm we are using non-iterative style for learning we are implementing a network which is wider and shallow which plays a important role in generalizing the overall performance which in turn boosts up the learning speed
