递归逻辑程序的强构造分层学习算法及其实现

本文提出了一种逻辑程序的强构造分层学习算法 ,用该算法解决了一类递归逻辑程序的强构造二层学习问题 ,为强构造学习研究提供了一个新的思路 。

递归神经网络的卡尔曼滤波及分层学习算法

将卡尔曼滤波算法引入到递归神经网络的训练当中，并针对递归神经网络、卡尔曼算法及ＢＰ算法的特点，提出了用于递归神经网络的分层学习算法，并给出了理论分析．仿真结果证明了本算法的有效性．

基于Actor-Critic框架的渗透测试路径技术研究

该文将层次强化学习与渗透测试相结合,将渗透测试过程建模为半马尔可夫决策模型,训练Agent在模拟网络环境中完成渗透测试路径发现,并提出了一种改进的基于Actor-Critic框架改进的自动分层记忆AHM-DQN算法(Automatic hierarchical memory Deep Q Networks,AHM-DQN)。首先,在Actor网络中加入一个双向递归神经网络作为同一Agent的信息交换层;其次,在Critic网络加入其他种类的Agent信息来学习多种Agent协同策略。该算法在Actor-Critic算法上进行了以下改进:一是集成了自动分层功能,对任务目标和动作的选择进行自动分层,提高了算法的效率;二是结合记忆因子,帮助Agent有效地记忆和学习,解决奖励值的稀疏性问题,提高算法精度。该算法在学习效率和收敛速度上优于传统的分层学习算法,解决了渗透测试主要依靠人工进行的问题。

A new decision tree learning algorithm

In order to improve the generalization ability of binary decision trees, a new learning algorithm, the MMDT algorithm, is presented. Based on statistical learning theory the generalization performance of binary decision trees is analyzed, and the assessment rule is proposed. Under the direction of the assessment rule, the MMDT algorithm is implemented. The algorithm maps training examples from an original space to a high dimension feature space, and constructs a decision tree in it. In the feature space, a new decision node splitting criterion, the max-min rule, is used, and the margin of each decision node is maximized using a support vector machine, to improve the generalization performance. Experimental results show that the new learning algorithm is much superior to others such as C4. 5 and OCI.