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In this paper, a novel supervised architecture for binary classification based on local modelling and information theory is described. The architecture is composed of two steps: in the first one, a separating borderline between the two classes is piecewise constructed by a set of centroids calculated by a modified clustering algorithm, based on information theory; each of these centroids define a...
Learning Vector Quantization (LVQ) is a popular class of nearest prototype classifiers for multiclass classification. Learning algorithms from this family are widely used because of their intuitively clear learning process and ease of implementation. They run efficiently and in many cases provide state of the art performance. In this paper we propose a modification of the LVQ algorithm that addresses...
How to choose a proper number of the neighbors is an important issue of the locally linear embedding algorithm. To investigate this issue, we propose an optimized locally linear embedding algorithm with adaptive neighbors (ANLLE). The ANLLE selects the neighbors with a locally adaptive criterion. In addition, a new data point mapping method that computes the low-dimensional description of the correspondents...
This article presents a method for the calculating similarity of two trajectories. The method is especially designed for a situation where the points of the trajectories are distributed sparsely and at non-equidistant intervals. The proposed method is based on giving different weights to different points: points that are close to each other get smaller weights than the points that do not have neighbors...
In this paper we evaluate k-nearest neighbor (KNN), linear and quadratic discriminant analysis (LDA and QDA, respectively) for embedded, online feature fusion which poses strong limitations on computing resources and timing. These algorithms are implemented on our multisensor data fusion (MSDF) architecture and are applied to traffic monitoring, i.e., classifying vehicles using distributed image,...
In this paper I present an environment and algorithm for lazy (incremental) construction of multigram profile models as part of IR (information retrieval) training and exploitation processes. N-grams are traditionally used for natural language text models, but they can be also successfully used for domain independent document classification. I am demonstrating results of a prototype utility which...
Money laundering (ML) is a serious crime which makes it necessary to develop detection methods in transactions. Some researches have been carried on, but the problem is not thoroughly solved. Aiming at the low detection rate of suspicious transaction at home and abroad in financial field, and with an analysis of radial basis function (RBF) neural network, we propose a radial basis function neural...
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