Machine Learning And Optimization Design Of Neural Network Classifier

Machine Learning makes a target of automatic retrieval and produce of knowledge. It has become one of the key areas in artificial intelligence and machine learning. The optimization design of classifier also is nuclear question in field of Machine Learning, Pattern Recognition and Data Mining, it has wide application in image recognition, speech understanding, medical treatment diagnosis and classification of web page. To improve the adaptation to the environment of classifier is the key problem for optimization design of classifier. Making use of machine learning methods to realize optimization design of classifier becomes an important research topic in machine learning and artificial intelligence.The research topic of this paper is machine learning and its applications in design neural network classifier. It has been focused on methods of optimization design of neural network classifier's structure and samples selection. The research topic of this paper has 4 parts as follow:1) From the point of design classifier view, the questions of the develop way and problem of machine learning have been discussed, some new methods appeared in recent years have been analyzed and researched.2) Research of optimization design of classifier's structure. Based on manifold learning. A novel approach of designing of neural networks based on parameter space in the low-dimension manifold was proposed to solve the problems about neural networks design rationally, which is used in recognition and classification of congener samples with non-linear configuration. This method based on manifold learning combines Sammon stress in order to estimate the value of parameter space in low-dimension, furthermore this value corresponds with the number of hidden in neural networks.3) Research of optimization selection of classifier's samples based on active learning. A novel approach of active learning based on fuzzy neural network classifier was proposed to solve the problems of surprisingly time consuming and costly in sample collection and annotation. Two new concepts of Min-Max Margin Based Approach and Uncertainty threshold on samples were introduced as a rule of active sample selecting to guarantee the most informative samples annotated. Therefore, the annotation and time cost were greatly reduced.4) Design of fuzzy neural network classifier of unlabeled sample. A novel kind of Ordination-Fuzzy min-max neural network (OFMM) based on non-metric multidimensional scaling (MDS) was proposed to solve the classification problems of unlabeled input pattern. Firstly, all the input patterns were sorted by MDS to get their similarity measures. Then these measures were used to supervise the following expansion and contraction stage of hyperboxes for classification. OFMM had improvements both in the validity of unlabelled patterns classification, and the network structure and training time.