| 本文已被：浏览 4789次 下载 5791次 |
码上扫一扫！ |
|
|
| 混合型学习模型HLM中的增量学习算法 |
|
陈兆乾1,2, 周志华1,2, 李红兵1,2, 谢俊元1,2
|
|
1.南京大学计算机科学与技术系,南京,210093;2.南京大学计算机软件新技术国家重点实验室,南京,210093
|
|
| 摘要: |
| 混合型学习模型HLM将概念获取算法HMCAP和神经网络算法FTART有机结合，能学习多概念和连续属性，其增量学习算法建立在二叉混合判定树结构和FTART网络的基础上，在给系统增加新的实例时，只需进行一遍增量学习调整原结构，不用重新生成判定树和神经网络，即可提高学习精度，速度快、效率高．本文主要介绍该模型中的增量学习算法． |
| 关键词: 混合模型 增量学习 神经网络 |
| DOI： |
| 分类号: |
| 基金项目:本文研究得到国家自然科学基金资助. |
|
| THE INCREMENTAL LEARNING ALGORITHM IN HYBRID LEARNING MODEL HLM |
|
CHEN Zhaoqian,ZHOU Zhihua,LI Hongbing,XIE Junyuan
|
| Abstract: |
| The multi-concept acquisition algorithm HMCAP and the neural network algorithm FTART are integrated in hybrid learning model HLM，which can deal with multiple concepts and continuous attributes．In this paper，the incremental learning algorithin of HLM which based on the structure of hybrid binary decision tree and FTART network is proposed．It has the ability of adjusting old structure to improve learning accuracy by once learning instead of rebuilding the decision tree and the neural networks when the new examples were provided． |
| Key words: Hybrid model incremental learning neural network |
