Improving Feature Map Quality of SOM Based on Adjusting the Neighborhood Function

This chapter presents a study on improving the quality of the self-organizing map (SOM). We have synthesized the relevant research on assessing and improving the quality of SOM in recent years, and then proposed a solution to improve the quality of the feature map by adjusting parameters of the Gaussian neighborhood function. We have used quantization error and topographical error to evaluate the quality of the obtained feature map. The experiment was conducted on 12 published datasets and compared the obtained results with some other improving neighborhood function methods. The proposed method received the feature map with better quality than other solutions.

Color and Texture Feature Based Scene Classification

Right now, overall trademark based multiscale multiresolution multistructure (M3LBP) neighborhood parallel example and nearby characteristic based totally min blend feature extraction is proposed for scene category. To extract international functions, characterize the leading spatial features in a couple of scale, a couple of choice, more than one structure way. The micro/macro shape facts and rotation invariance are guaranteed inside the worldwide function extraction approach. Neighborhood function extraction, coloration histogram characteristic (CHF) can thoroughly explain the spatial coloration statistics of an image. It also describes the image brightness, color statistics of a photo, which encompass the picture coloration distribution, photo assessment. The CHF can be computed from the min max shade quantizes. Ultimately Fused feature instance amongst nearby and international capabilities because the scene descriptor to prepare a portion based absolutely extreme finding a workable pace for scene style is outfitted. The proposed strategy is radically assessed on benchmark scene datasets (the 21 magnificence land use scene), and the trial results show that the proposed procedure prompts predominant kind standard execution as contrasted and the realm of-work of art style systems.

Approximating the Temporal Neighbourhood Function of Large Temporal Graphs

Temporal networks are graphs in which edges have temporal labels, specifying their starting times and their traversal times. Several notions of distances between two nodes in a temporal network can be analyzed, by referring, for example, to the earliest arrival time or to the latest starting time of a temporal path connecting the two nodes. In this paper, we mostly refer to the notion of temporal reachability by using the earliest arrival time. In particular, we first show how the sketch approach, which has already been used in the case of classical graphs, can be applied to the case of temporal networks in order to approximately compute the sizes of the temporal cones of a temporal network. By making use of this approach, we subsequently show how we can approximate the temporal neighborhood function (that is, the number of pairs of nodes reachable from one another in a given time interval) of large temporal networks in a few seconds. Finally, we apply our algorithm in order to analyze and compare the behavior of 25 public transportation temporal networks. Our results can be easily adapted to the case in which we want to refer to the notion of distance based on the latest starting time.

Self-Organizing Map Convergence

Self-organizing maps are artificial neural networks designed for unsupervised machine learning. Here in this article, the authors introduce a new quality measure called the convergence index. The convergence index is a linear combination of map embedding accuracy and estimated topographic accuracy and since it reports a single statistically meaningful number it is perhaps more intuitive to use than other quality measures. The convergence index in the context of clustering problems was proposed by Ultsch as part of his fundamental clustering problem suite as well as real world datasets. First demonstrated is that the convergence index captures the notion that a SOM has learned the multivariate distribution of a training data set by looking at the convergence of the marginals. The convergence index is then used to study the convergence of SOMs with respect to the different parameters that govern self-organizing map learning. One result is that the constant neighborhood function produces better self-organizing map models than the popular Gaussian neighborhood function.