混合型多概念获取算法的设计及其抗噪音能力

IHMCAP(incremental hybrid multi-concepts acquisit ion procedure)算法将基于概率论的符号学习与神经网络学习相结合,通过引入FTART(field theory-based adaptive resonance theory)神经网络,成功地解决了符号学习与神经网络 学习精度之间的均衡性问题,实现了两种不同思维层次的靠近.该算法采用一种独特的增量学 习机制,当增加新的实例时,只需进行一遍增量学习,调整原结构,不必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.同时,这种增量学习机制还可以降低算法对噪音数 据的敏感度,从而使IHMCAP可以应用于实时在线学习任务.

Design and Noise Resistance Ability of Incremental Hybrid Multi-concepts Acquisition Algorithm

IHMCAP (incremental hybrid multi-concepts acquisition) algorithm combines the p robability based symbolic learning with neural learning. The balance of learning accuracy between the symbolic and the neural parts are proportioned successfull y, and the two different levels of thought are aboard laid by adhibiting FTART ( field theory-based adaptive resonance theory) neural network. A unique incremen tal learning mechanism is employed with this algorithm, which can adjust the fo rmer structure to improve learning accuracy by learning once instead of rebuildi ng the decision tree and the neural networks when the new examples are provided. It has higher speed, and is efficient. Moreover, the noisy sensibility of the s ystem is depressed by the incremental learning mechanism, which enables IHMCAP c an be applied to the tasks that require real-time online learning.