1736548
Image Region Selection and Ensemble for Face Recognition
In this paper, a novel framework for face recognition, namely Selective Ensemble of Image Regions (SEIR), is proposed. In this framework, all possible regions in the face image are regarded as a certain kind of features. There are two main steps in SEIR: the first step is to automatically select several regions from all possible candidates; the second step is to construct classifier ensemble from the selected regions. An implementation of SEIR based on multiple eigenspaces, namely SEME, is also proposed in this paper. SEME is analyzed and compared with eigenface, PCA + LDA, eigenfeature, and eigenface + eigenfeature through experiments. The experimental results show that SEME achieves the best performance.
7567561
Genetic Algorithm based Selective Neural Network Ensemble
Neural network ensemble is a learning paradigm where several neural networks are jointly used to solve a problem. In this paper, the relationship between the generalization ability of the neural network ensemble and the correlation of the individual neural networks is analyzed, which reveals that ensembling a selective subset of individual networks is superior to ensembling all the individual networks in some cases. Therefore an approach named GASEN is proposed, which trains several individual neural networks and then employs genetic algorithm to select an optimum subset of individual networks to constitute an ensemble. Experimental results show that, comparing with a popular ensemble approach, i.e. averaging all, and a theoretically optimum selective ensemble approach, i.e. enumerating, GASEN has preferable performance in generating ensembles with strong generalization ability in relatively small computational cost.
10964754
SETRED: Self-training with Editing
Self-training is a semi-supervised learning algorithm in which a learner keeps on labeling unlabeled examples and retraining itself on an enlarged labeled training set. Since the self-training process may erroneously label some unlabeled examples, sometimes the learned hypothesis does not perform well. In this paper, a new algorithm named Setred is proposed, which utilizes a specific data editing method to identify and remove the mislabeled examples from the self-labeled data. In detail, in each iteration of the self-training process, the local cut edge weight statistic is used to help estimate whether a newly labeled example is reliable or not, and only the reliable self-labeled examples are used to enlarge the labeled training set. Experiments show that the introduction of data editing is beneficial, and the learned hypotheses of Setred outperform those learned by the standard self-training algorithm.
31771496
Spiculated Lesion Detection in Digital Mammogram Based on Artificial Neural Network Ensemble
Among breast abnormalities, spiculated lesions are one of the most difficult type of tumor to detect. In this paper, we apply a feature extraction method to generate four feature images for a single mammogram, and then partition every feature image into a series of small square blocks. The four average feature values of each block are considered as an instance describing the block. Finally we use an artificial neural network ensemble method to detect the spiculated lesions. Experiments show that the accuracy of this method is well on digital mammograms.
16987945
Mining typhoon knowledge with neural networks
Neural network technology has not been fully utilised in data mining. There are two reasons for this. Firstly, most neural algorithms need long-term training, and cannot perform incremental learning. Secondly, the knowledge learned by neural networks is concealed within a large quantity of connections. We have develop a neural network method to mine typhoon knowledge which overcomes those two obstacles. Experimental results show that our method shows considerable promise.
15520573
Hybrid decision tree
In this paper, a hybrid learning approach named hybrid decision tree (HDT) is proposed. HDT simulates human reasoning by using symbolic learning to do qualitative analysis and using neural learning to do subsequent quantitative analysis. It generates the trunk of a binary HDT according to the binary information gain ratio criterion in an instance space defined by only original unordered attributes. If unordered attributes cannot further distinguish training examples falling into a leaf node whose diversity is beyond the diversity-threshold, then the node is marked as a dummy node. After all those dummy nodes are marked, a specific feedforward neural network named Fannc that is trained in an instance space defined by only original ordered attributes is exploited to accomplish the learning task. Moreover, this paper distinguishes three kinds of incremental learning tasks. Two incremental learning procedures designed for example-incremental learning with different storage requirements are provided, which enables HDT to deal gracefully with data sets where new data are frequently appended. Also a hypothesis-driven constructive induction mechanism is provided, which enables HDT to generate compact concept descriptions.
205343152
On queuing lengths in on-line switching
Queues that temporarily store fixed-length packets are ubiquitous in network switches. Scheduling algorithms that prevent packet-loss are always desirable. LONGEST-QUEUE-FIRST (LQF) is an on-line greedy algorithm widely exploited because of its simplicity and efficiency. In this paper, we give improved bounds on the competitive ratio of LQF in terms of the worst-case queuing length, parameterized with respect to the optimal queuing length of a clairvoyant adversary. This gives a better picture of LQF's performance under heavy traffic than the usual (unparameterized) competitive ratio. We also discuss randomization, and we conclude with some intriguing open problems regarding a two-dimensional generalization of the problem.
8244593
A study on polynomial regression and Gaussian process global surrogate model in hierarchical surrogate-assisted evolutionary algorithm
This paper presents a study on hierarchical surrogate-assisted evolutionary algorithm (HSAEA) using different global surrogate models for solving computationally expensive optimization problems. In particular, we consider the use of Gaussian process (GP) and polynomial regression (PR) methods for approximating the global fitness landscape in the surrogate-assisted evolutionary search. The global surrogate model serves to pre-screen the EA population for promising individuals. Subsequently, these potential individuals undergo a local search in the form of Lamarckian learning using online local surrogate models. Numerical results are presented on two multimodal benchmark test functions. The results obtained show that both PR-HSAEA and GP-HSAEA converge to good designs on a limited computational budget. Further, our study also shows that the GP model is suitable for global surrogate modeling in HSAEA if the evaluation function is very expensive in computations. On moderately expensive problems, the PR method may serve to generate better efficiency than using GP.