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The current trend of growth of information reveals that it is inevitable that large-scale learning problems become the norm. In this paper, we propose and analyze a novel Low-density Cut based tree Decomposition method for large-scale SVM problems, called LCD-SVM. The basic idea here is divide and conquer: use a decision tree to decompose the data space and train SVMs on the decomposed regions. Specifically,...
Many risk factors affect software development and risk management has become one of the major activities in software development. Discovering causal directions among risk factors and project performance are important support for risk management. The Additive Noise Model (ANM) is an effective algorithm for discovering the direction on one-to-one causalities, but ineffective on many-to-one causalities...
There has been growing interest in developing more effective learning machines for tensor classification. At present, most of the existing learning machines, such as support tensor machine (STM), involve nonconvex optimization problems and need to resort to iterative techniques. Obviously, it is very time-consuming and may suffer from local minima. In order to overcome these two shortcomings, in this...
Non-Hamiltonian graph is featured by the degree sequence that fails to meet the Chvátal condition. By analyzing this feature, a new function of the edge bound for non-Hamiltonian graph is proposed in this paper. The maximum number of edges in maximal non-Hamiltonian graph is derived from this function with mathematical analysis for extreme values, which not only implies the characters of maximal non-Hamilton...
Segmentation is a process to obtain the desirable features in image processing. However, the existing techniques that use the multilevel thresholding method in image segmentation are computationally demanding due to the lack of an automatic parameter selection process. This paper proposes an automatic parameter selection technique called an automatic multilevel thresholding algorithm using stratified...
One of the challenging tasks in AI is to read and do reasoning on languages in an automatic way as the human does. We present an intelligent inference system to predict people's activities from English sentences in a simulated human memory model. Our system analyses input sentences with refined natural language processing technologies and stores the extracted information as new memory in a simulated...
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