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There has been a surge in research interest in learning feature representation of networks in recent times. Researchers, motivated by the recent successes of embeddings in natural language processing and advances in deep learning, have explored various means for network embedding. Network embedding is useful as it can exploit off-the-shelf machine learning algorithms for network mining tasks like...
This paper puts forward a way to design and realize a teaching quality evaluation system for part-time teachers. Through the evaluation of part-time teachers in the teaching process, the quantitative evaluation results of the teaching quality of the course can be gained on the basis of analytic hierarchy process to carry on the distribution of evaluation index weights, fuzzy comprehensive evaluation...
Fine-grained object recognition is more challenging than generic categorization due to the subtle difference between subcategories under the large intra-class pose change and appearance variations. The state-of-the-art fine-grained recognition methods usually utilize part detection or pose alignment to alleviate the pose variation, and then use convolutional neural networks (CNNs) to extract local...
Mining closed frequent item set(CFI) plays a fundamental role in many real-world data mining applications. However, memory requirement and computational cost have become the bottleneck of CFI mining algorithms, particularly when confronting with large scale datasets, which herewith makes mining closed frequent item set from large scale datasets a significant and challenging issue. To address the above...
Mining closed frequent itemset (CFI) plays an essential role in many real-world data mining applications. With the emergence of abundant large-scale data sets, it now turns to be a significant and challenging issue to mine CFI concurrently. This paper proposes a parallel balanced mining algorithm for CFI based on the MapReduce platform. The proposed algorithm adopts Greedy strategy to group items...
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