45083530
Improving backpropagation learning under limited precision
Abstract Most of the work to date on learning in neural networks assumes the availability of unlimited precision for implementing weights and the neural transfer functions. Recently some studies have investigated the effects of limited precision on the learning and generalisation performance. In this paper, we identify the causes of performance degradation due to limited precision and propose a novel algorithm to improve the performance of Backpropagation.
8311095
Classifying image texture with statistical landscape features
This paper proposes to use three-dimensional information derived from the graph of an image function for texture description. The graph of an image function is a rumpled surface appearing like a landscape. To characterize the texture through this landscape, six novel texture feature curves based on the statistics of the geometrical and topological properties of the solids shaped by the graph and a variable horizontal plane are used. The proposed statistical landscape features have been shown by systematic experiments to offer very low error rates on a large subset of the Brodatz texture album having excluded some nonhomogeneous images, the entire Brodatz texture set, as well as the VisTex texture collection.
40089095
Ray-guided global optimization method for training neural networks
Abstract A novel method for globally searching to find the good minima is proposed in this paper. Starting from a local minimum, the weight space around it is scanned with the process being guided by terrain-independent emanating rays. During the search, starting points for further exploration are identified and used to find corresponding local minima. Based on the correct classification rate (CCR) on the validation data, the best minimum is found.
205904713
Fuzzy aggregated connectedness for image segmentation
Fuzzy methodology has been successfully applied to image analysis and pattern recognition since the theory of fuzzy set was 0rst introduced by Zadeh [1]. It is believed that the fuzzy set theory models the inherent imprecision in images. Fuzzy geometrical approaches focus on the spatial measures and have attracted considerable research interest in handling the spatial uncertainty in images [2]. Fuzzy connectedness is an important topological property of images. The classical de0nition was given by Rosenfeld [3]. A modi0cation to the traditional concept of fuzzy connectedness was further proposed [4]. Based on the notion of fuzzy connectedness of image elements, fuzzy objects for n-dimensional digital spaces was exploited for image segmentation [5]. Fuzzy connectedness is a natural extension to the crisp connectedness and re:ects the imprecise topological structures of images. The algorithms in the literature usually compute the fuzzy connectedness with regard to the best path between a pair of points. This approach su;ers from high sensitivity to noise. Noise in corrupted images may misguide the detection of the best path and further in:uence the determination of fuzzy connectedness. We believe that this has mainly been due to the binarization in determining the best path. The information of a path is either fully preserved or discarded and this contradicts the concept of fuzzy sets. In this paper,
37744500
Improving Nearest Neighbor Classification with Simulated Gravitational Collapse
The performance of the Nearest Neighbor classifier drops significantly with the increase of the overlapping of the distribution of different classes. To overcome this drawback, we propose to simulate the physical process of gravitational collapse to trim the boundaries of the distribution of each class to reduce overlapping. The proposed simulated gravitational collapse(SGC) algorithm is tested on 7 real-world data sets. Experimental results show that the nearest prototype classifier based on SGC outperforms conventional NN and k-NN classifiers.
10274461
Pattern Classification Using Rectified Nearest Feature Line Segment
This paper proposes a new classification method termed Rectified Nearest Feature Line Segment (RNFLS). It overcomes the drawbacks of the original Nearest Feature Line (NFL) classifier and possesses a novel property that centralizes the probability density of the initial sample distribution, which significantly enhances the classification ability. Another remarkable merit is that RNFLS is applicable to complex problems such as two-spirals, which the original NFL cannot deal with properly. Experimental comparisons with NFL, NN(Nearest Neighbor), k-NN and NNL (Nearest Neighbor Line) using artificial and real-world datasets demonstrate that RNFLS offers the best performance.
894881
Performance improvement of TCP Vegas over heterogeneous networks
Current IP network has become the dominant paradigm for all networking environments. The significant cause of packet losses in such heterogenous networks is no longer limited to network congestion. Traditional TCP interprets every packet loss as caused by congestion which may be not the case in the current Internet. Misinterpretation of wireless random loss as an indication of network congestion results in TCP slowing down its sending rate unnecessarily. We propose a new variant of TCP Vegas named RedVegas. By using the innate nature of Vegas and congestion indications marked by routers, RedVegas may detect random packet losses precisely. Through the packet loss differentiation, RedVegas reacts appropriately to the losses, and therefore the throughput of connection over heterogeneous networks can be significantly improved.
11560820
Introducing recombination with dynamic linkage discovery to particle swarm optimization
In this paper, we introduce the recombination operator with the technique of dynamic linkage discovery to particle swarm optimization (PSO) in order to improve the performance of PSO. Numerical experiments are conducted on a set of carefully designed benchmark functions and demonstrate good performance achieved by the proposed methodology.
2968797
Ego-motion estimation using optical flow fields observed from multiple cameras
In this paper, we consider a multi-camera vision system mounted on a moving object in a static three-dimensional environment. By using the motion flow fields seen by all of the cameras, an algorithm which does not need to solve the point-correspondence problem among the cameras is proposed to estimate the 3D ego-motion parameters of the moving object. Our experiments have shown that using multiple optical flow fields obtained from different cameras can be very helpful for ego-motion estimation.
5860855
A distributed buffer management approach supporting IPv6 mobility
In wireless local area networks (WLAN), mobility support is essential for providing seamless services. The current Mobile IP standard suffers several problems, especially for accommodating real-time media applications. Most of the existing studies focus on improving the performance between the home agent (HA) and foreign agent (FA). In this work, we propose a buffer management approach to enhance the performance between the mobility agent (HA or FA) and the mobile node (MN). The proposed scheme deals with the packet delivery between MA and MN in Layer 2. Through the simulation, we show that our scheme improves TCP throughput as well as reducing the UDP packet loss rate. Moreover, its implementation is feasible in both IPv4 and IPv6, and suitable for supporting quality of service (QoS).
12984676
Hybrid Evolutionary And Annealing Algorithms For Nonlinear Discrete Constrained Optimization
This paper presents a procedural framework that unifies various mechanisms to look for discrete-neighborhood saddle points in solving discrete constrained optimization problems (DCOPs). Our approach is based on the necessary and sufficient condition on local optimality in discrete space, which shows the one-to-one correspondence between the discrete-space constrained local minima of a problem and the saddle points of the corresponding Lagrangian function. To look for such saddle points, we study various mechanisms for performing ascents of the Lagrangian function in the original-variable subspace and descents in the Lagrange-multiplier subspace. Our results show that CSAEA, a combined constrained simulated annealing and evolutionary algorithm, performs well when using mutations and crossovers to generate trial points and accepting them based on the Metropolis probability. We apply iterative deepening to determine the optimal number of generations in CSAEA and show that its performance is robust with respect to changes in population size. To test the performance of the procedures developed, we apply them to solve some continuous and mixed-integer nonlinear programming (NLP) benchmarks and show that they obtain better results than those of existing algorithms.