机器学习和神经网络学习中的若干问题研究-计算数学专业论文.docxVIP

大连理工大学博士学位论文Research 大连理工大学博士学位论文 Research on Some Problems in Machine Learning and Neural Network Learning Abstract In machine leaming，the learning processes are usually divided into supervised learning and unsupervised learning depending on whether the labeled samples are used．As an essential compo— nent of machine learning，feed-forward neural network plays an important role in pattern recog— nition and function approximation．For supervised learning，training data are first mapped into hidden layer via input layer,and then are mapped into output layer after activated by activation function in the hidden layer,and finally certain learning rules are used to adjust the weights of the network．The structure of a feed forward network involves the number of input units，hidden units and output units，as well as the weights connection method among these units．Since the learn— ing speed and generalization performance of a neural network is closely related to the network structure and properties of the training samples，We focus on in this dissertation the optimization methods of single．．hidden layer feed--forward network and generalization performance of learning process for time—dependent samples．The structure of this dissertation is organized as follows． In Chapter 1，some recent researches about machine learning and neural network learning， optimization of feed forward neural network and generalization performance of samples learning are introduced． In Chapter 2，since the dimension of samples is generally identical to the number of input units of a feed．forward neural network，we mainly study the attribute reduction method based on the rough sets theory．In particular,we present new attribute reduction algorithm based on crisp set operations，in which traditional reduction operations such as conjunction law and disjunction law are converted to basic operations of crisp set．The instance shows that the proposed algorithm can effectively cut down the dimension of input samples．In