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The Euclid distance based K-means clustering is among the hard classification algorithms. When dealing with deterministic remote sensing data, it is difficult to gain satisfactory classification results using K-means algorithm. The traditional K-means clustering algorithm is faced with several shortcomings such as locally converged optimization, being sensitive to initial clustering centers, etc....
This paper firstly introduces the basic concept of covering algorithm and kernel covering algorithm (KCA) adopting kernel function, then analyzes the influence of proximity principle used to judge rejection points on classifier's effect. FKCA, i.e. Fuzzy Kernel Covering Algorithm, is proposed to improve the performance of classifier. The main improvement of FKCA is the change of radius selection and...
This paper addresses the decision trees induction with uncertain data. In other words, it presents a novel method, called uncertain decision trees (UDT) to handle the uncertainty during the process of inducing decision trees. Here, uncertainty is depicted via cloud model theory, a quantitative-qualitative transforming model with uncertainty, which can well integrate the fuzziness and randomness of...
This paper proposes a fast and robust algorithm for traffic sign detection and recognition. The algorithm includes two stages: traffic sign detection and recognition. In the first stage, Adaboost algorithm based red pixels model of speed limit sign in the Lab color space is built. Then the model is used to extract area of latent speed limit signs. After that, the improved Hough Transform is used to...
In this paper, the algorithm of a kind of hierarchical competitive covering networks for the classification problems is proposed based on the quotient space theory, which defines granularity according to Huffman coding. Not only three classes of secondary structure but also eight classes are discussed for the protein. Instances show that this kind of networks improves the sorting ability of covering...
This paper develops a cascade of linear SVM classifiers for fast object detection. The learning problem of every node in the cascade structure is described as a new quadratic programming problem in the frame of SVM, which makes every linear classifier achieve very high detection rate but only moderate false positive rate. The real experiment shows that this method enjoys good generalization capacity...
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