Unsupervised Multi-modal Learning

被引：2
|
作者：
Iqbal, Mohammed Shameer [1 ]
机构：
[1] Acadia Univ, Jodrey Sch Comp Sci, Wolfville, NS B4P 2R6, Canada
来源：
ADVANCES IN ARTIFICIAL INTELLIGENCE (AI 2015) | 2015年 / 9091卷
关键词：
Unsupervised learning; Associative memory; Restricted boltzmann machines; Multi-modal data; Sensory-motor learning;
D O I：
10.1007/978-3-319-18356-5_32
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We present an unsupervised deep belief network that can learn from multiple channels and is capable of dealing with missing information. The network learns transferable features to accommodate the addition of a new channel using a combination of unsupervised learning and a simple back-fitting.
引用
收藏
页码：343 / 346
页数：4
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