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In this paper, a new algorithm for visualization of high-multidimensional data is described. The algorithm follows several steps. At first, centers representing several categories are selected, and Euclidean distances between these centers are calculated in a high-dimensional space. Then these centers are placed in a 2-dimensional space in such a way that distances in this 2-dimensional space are...
Fuzzy integral is an aggregation tool for classification, which is used to improve the accuracy and robustness of the fusion of multiple systems. Multi-classifier fusion, based on Fuzzy Integrals measure system, will have a great impact on the performance of the fusion system. If well defined, the fuzzy measures could markedly improve the classification accuracy; conversely, it may even result in...
This paper presents a genetic algorithm (GA) approach for parameters optimization of support vector machine, which is used for the object-oriented classification of high spatial resolution images over urban area. The proposed method is a three-step routine involves the integration of 1) image segmentation, 2) GA-based parameter optimization of Support vector machine (SVM), and 3) objected-based classification...
An adaptive dimensionality reduction method to conduct classification of hyper-spectral imagery using optimal segmentation of spectral signature is proposed. The method partitions the spectral signals into a fixed number of contiguous intervals with constant intensities in terms of minimizing the mean square error. To automatically obtain the best number of the segments, a quantitative indictor based...
Nearest Neighbor Classifier is one of the most classical lazy learning schemes. The basic nearest neighbor classifiers suffer from the common problem that the instances used to train the classifier are all stored indiscriminately, and as a result, the required memory storage is huge and response time becomes slow with a large database. In this paper, a new Instances Selection algorithm based on Classification...
MCS (minimal consistent set) is one of the classical algorithms for minimal consistent subset selection problem. However, when noisy samples are present classification accuracy can suffer. In addition, noise affect the size of minimal consistent set. Therefore, removing noise is an important issue before sample selection. In this paper, an improvement approach based on MCS to select the representative...
A large variety of image features has been invented for detection of objects of a known class. We propose a framework to optimize the discrimination-efficiency tradeoff in integrating multiple, heterogeneous features for object detection. Cascade structured detectors are learned by boosting local feature based weak classifiers. Each weak classifier corresponds to a local image region, from which several...
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