Quotient space model based hierarchical machine learning

被引：0
|
作者：
Ling, Z
Bo, Z
机构：
来源：
PROCEEDINGS OF THE 2005 INTERNATIONAL CONFERENCE ON NEURAL NETWORKS AND BRAIN, VOLS 1-3 | 2005年
关键词：
quotient space; machine learning; data mining; hierarchical structure;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We proposed a quotient space based model that can represent the world at different granularities and can be used to handle problems hierarchically. The model can be used in two different ways: top-down deduction and bottom-up induction. In this paper, we will discuss the quotient space model based bottom-up induction, i.e., hierarchical learning. Some approaches for learning the structural knowledge from data are presented. The main advantage of hierarchical induction is its efficiency, that is, the whole structure of data can be abstracted at once.
引用
收藏
页码：PL81 / PL83
页数：3
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