IMPROVEMENT OF CONVERGENCE OF LEARNING OF MULTI-LAYER NEURAL NETWORKS AND APPLICATION FOR SEARCH ENGINE

在本研究中，我们研究了多层神经网络学习收敛性的改进及其在搜索引擎中的应用。结果摘要如下：(1)根据类中数据的个数进行分类。研究了更新连接权值的概率与平均等效误差成正比的学习方法。它保持了表示大误差和小误差的数据数量的平衡，然后少数类变得可学习。(2)获得早期对称制动的学习方法。我们研究了多层神经网络仅更新一个连接权值以避免学习停止的学习方法。(3)带边际的感知器学习。我们引入了加德纳边际来改进感知器学习。我们的算法在学习的早期阶段优于赫布学习。(4)通过线性学习机进行集成学习分析。我们分析了集成学习的泛化误差与弱学习器K个数的关系。结果表明，在K趋于无穷大时，泛化误差是单个百分子泛化误差的一半。

In this study, we investigated improvement of convergence of learning of the multi-layer neural networks and its application for search engine. Abstract of the results are follows :(1) Baised Classification with number of the data in the class. Learning method of probability of updating the connection weight is proportional to the mean equated error, is investigated. It keeps balance of the number of the data express large error and small error, then minority class becomes learnable.(2)Learning method to obtain Early symmetry braking.We investigated the learning method for mutilayer neural networks updating only one connection weight to avoid the stopping of the learning.(3)Perceptron learning with a margin.We introduced a margin a la Gardner to improve the perceptron learning. Our algorithm is superior to Hebbian learning at the early stage of the learning.(4)Analysis of ensemble learning through linear learning machine.We analyzed generalization error of ensemble learning related to the number of the weak learner K. As the result, it has been shown that at the limit of K goes to infinity, the generalization error is a half of that of single percentron.