A neural support vector machine

被引：5
|
作者：
Jandel, Magnus [1 ,2 ]
机构：
[1] Agora Biosyst, SE-19322 Sigtuna, Sweden
[2] Swedish Def Res Agcy, SE-16490 Stockholm, Sweden
来源：
NEURAL NETWORKS | 2010年 / 23卷 / 05期
关键词：
Support vector machine; Neural systems; Pattern recognition; Perceptual learning; Associative memory; Olfactory system; OLFACTORY SYSTEM; ODOR RECOGNITION; NETWORKS; ALGORITHMS; CORTEX; MEMORY;
D O I：
10.1016/j.neunet.2010.01.002
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Support vector machines are state-of-the-art pattern recognition algorithms that are well founded in optimization and generalization theory but not obviously applicable to the brain. This paper presents Bio-SVM, a biologically feasible support vector machine. An unstable associative memory oscillates between support vectors and interacts with a feed-forward classification pathway. Kernel neurons blend support vectors and sensory input. Downstream temporal integration generates the classification. Instant learning of surprising events and off-line tuning of support vector weights trains the system. Emotion-based learning, forgetting trivia, sleep and brain oscillations are phenomena that agree with the Bio-SVM model. A mapping to the olfactory system is suggested. (C) 2010 Elsevier Ltd. All rights reserved.
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页码：607 / 613
页数：7
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