Performance analysis of associative memory using the maximal margin learning
Performance analysis of associative memory using the maximal margin learning
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发表时间：
2006-07
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通讯作者：
Hitoshi Akimoto;M. Hattori
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作者：
Hitoshi Akimoto;M. Hattori
虽然原始的Hopfield联想记忆（HAM）算法存储容量和鲁棒性较差，但传统的学习算法大多只关注存储容量或学习速度的提高，而很少考虑鲁棒性。我们已经提出了一种新的学习算法的基础上的最大间隔的角度，这是在学习的支持向量机。通过该方法学习的HAM可以大大提高降噪效果。在本文中，我们揭示了所提出的方法学习的HAM的特点。计算机仿真结果表明，该方法具有比传统方法更优越的上级性能。
Although the original Hopfield Associative Memory (HAM) causes poor storage capacity and robustness, most of the conventional learning algorithms focus on only improvement in storage capacity or learning speed, and the robustness is hardly considered. We have already proposed a novel learning algorithm based on a maximal margin perspective, which is employed in learning of support vector machines. A HAM learned by the proposed method can much improve the noise reduction effect. In this paper we reveal characteristics of a HAM learned by the proposed method. Several computer simulations show the superior performances and properties of the proposed method to those of the conventional ones.