• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Cognitive Informatics and Soft Computing >Performance Analysis of Extreme Learning Machine Variants with Varying Intermediate Nodes and Different Activation Functions
【24h】

Performance Analysis of Extreme Learning Machine Variants with Varying Intermediate Nodes and Different Activation Functions

机译：具有不同中间节点的极端学习机变体的性能分析及不同的激活功能

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Feedforward Neural Networks are the type of Artificial Neural networks, which follow a unidirectional path. The input nodes are associated with the intermediate layers and the intermediate layers are associated with the output layer. There are no connections which feedback to the input or the intermediate layer and thus are different from the recurrent neural networks. Extreme Learning Machine (ELM) is an algorithm that has no feedback path and the data flows in a single direction, i.e., from input to output. ELM is an emerging algorithm and is widely used for but not limited to classification, clustering, regression, sparse approximation, feature learning, and compression with a single layer or multi-layers of intermediate nodes. The best-preferred standpoint of ELM is that there is no requirement for the intermediate layer factors to be tuned. The intermediate layer is randomly generated and is never updated thereafter.
机译：前馈神经网络是人工神经网络的类型，其遵循单向路径。输入节点与中间层相关联，中间层与输出层相关联。没有连接到输入或中间层的反馈，因此与经常性神经网络不同。极端学习机（ELM）是一种没有反馈路径的算法，并且数据在单个方向上流动，即从输入到输出。 ELM是一种新兴算法，并且广泛用于但不限于分类，聚类，回归，稀疏近似，特征学习和压缩，具有单层或多层中间节点。 ELM的最佳优选观点是，没有要求调整中间层因子。中间层是随机生成的，此后永远不会更新。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号