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Multi-label learning task is using to solve problems of syndrome diagnosis for patients may simultaneously have more than one syndrome in traditional Chinese medicine (TCM). The two goals of multi-label learning are label prediction loss and relevance ordering loss. Most Multi-label learning algorithms focus on only one of the goals and neglect the other one. However, there is a multi-label learning...
As the large number of feature attributes in Case-based reasoning system (CBR) brings a huge information redundancy which reduces the retrieval efficiency, a novel reduction method based on Water-Filling is proposed to remove those unnecessary attributes. In the method, the importance of each attribute could be calculated by utilizing the ratio of the standard deviation and the mean value of each...
In the application of case-based reasoning in multi-objective evaluation optimizing setting method, attributes weights of inputs in case retrieval are usually obtained subjectively, thus resulting in deterioration in accuracy of set points. To solve the problem, this paper presents a retrieval strategy based on group decision-making, in which, based on groups of weights by genetic algorithm, the retrieved...
Case retrieval is the focal stage in a Case-Based Reasoning (CBR) system. In this paper, a new case retrieval method called CRGCU is proposed in a systematical way, which is based on Genetic Algorithms (GAs) and Group Decision-Making method. First, feature attribute weights of cases are optimized by using GAs. Second, instead of utilizing only one set of feature attribute weights, we calculate similarity...
This paper is concerned with the design of a high speed current steering DAC. Techniques to improve static precision are preserved while their negative influences on dynamic performance are suppressed. The prototype is implemented with the SMIC 0.13 ??m process. With an update rate of 700 Msamples/s, measurements show that the DAC achieves over 40 dB SFDR under a sampling rate of 700 Ms/s and consumes...
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