This paper proposed an improved particle swarm optimization algorithm (IPSO) to solve continuous function optimization problems. Two improvement strategies named "Vector correction strategy" and "Jump o...
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Data indexing is common in data mining when working with high-dimensional, large-scale data sets. Hadoop, a cloud computing project using the MapReduce framework in Java, has become of significant interest in distribu...
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In our real world, there usually exist several different objects in one image, which brings intractable challenges to the traditional pattern recognition methods to classify the images. In this paper, we introduce a C...
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We study what we call semi-defined classification, which deals with the categorization tasks where the taxonomy of the data is not well defined in advance. It is motivated by the real-world applications, where the unl...
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As the Cloud computing appears to be part of the mainstream computing in a few years, the number of the services it provides, its users, and the requests of these services will be on the rise accordingly. To deliver s...
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In the process of power load forecasting, electricity experts always divide the forecasting situation into several categories, and the same category uses the same forecasting model. There exists such a situation that ...
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Traditional 1-best translation pipelines suffer a major drawback: the errors of 1- best outputs, inevitably introduced by each module, will propagate and accumulate along the pipeline. In order to alleviate this probl...
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Traditional 1-best translation pipelines suffer a major drawback: the errors of 1- best outputs, inevitably introduced by each module, will propagate and accumulate along the pipeline. In order to alleviate this problem, we use compact structures, lattice and forest, in each module instead of 1-best results. We integrate both lattice and forest into a single tree-to-string system, and explore the algorithms of lattice parsing, lattice-forest-based rule extraction and decoding. More importantly, our model takes into account all the probabilities of different steps, such as segmentation, parsing, and translation. The main advantage of our model is that we can make global decision to search for the best segmentation, parse-tree and translation in one step. Medium-scale experiments show an improvement of +0.9 BLEU points over a state-of-the-art forest-based baseline.
In this paper, we propose a novel manifold alignment method by learning the underlying common manifold with supervision of corresponding data pairs from different observation sets. Different from the previous algorith...
Cement rotary kiln calcining process is a kind of functional equipment for fuel combustion, heat exchange, and chemical reaction. A complex succession of chemical reactions takes place as the temperature rises. One ca...
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Minimum Error Rate Training (MERT) as an effective parameters learning algorithm is widely applied in machine translation and system combination area. However, there exists an ambiguity problem in respect to the train...
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